Free Google Professional-Machine-Learning-Engineer exam dumps Questions and Answers in PDF format

Answer: A
Explanation: The simplest and most efficient approach for preparing the data for AutoML is to use BigQuery and Vertex AI. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery can preprocess the data by using SQL functions such as filtering, aggregating, joining, transforming, and creating new features. The preprocessed data can be stored in a new table in BigQuery, which can be used as the data source for Vertex AI. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a BigQuery table, which can be used to train an AutoML model. Vertex AI can also evaluate, deploy, and monitor the AutoML model, and provide online or batch predictions. By using BigQuery and Vertex AI, users can leverage the power and simplicity of Google Cloud to train an AutoML model to predict house prices. The other options are not as simple or efficient as option A, for the following reasons: Option B: Using Dataflow to preprocess the data and write the output in TFRecord format to a Cloud Storage bucket would require more steps and resources than using BigQuery and Vertex AI. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. TFRecord is a binary file format that can store sequential data efficiently. However, using Dataflow and TFRecord would require writing code, setting up a pipeline, choosing a runner, and managing the output files. Moreover, TFRecord is not a supported format for Vertex AI managed datasets, so the data would need to be converted to CSV or JSONL files before creating a Vertex AI managed dataset. Option C: Writing a query that preprocesses the data by using BigQuery and exporting the query results as CSV files would require more steps and storage than using BigQuery and Vertex AI. CSV is a text file format that can store tabular data in a comma-separated format. Exporting the query results as CSV files would require choosing a destination Cloud Storage bucket, specifying a file name or a wildcard, and setting the export options. Moreover, CSV files can have limitations such as size, schema, and encoding, which can affect the quality and validity of the data. Exporting the data as CSV files would also incur additional storage costs and reduce the performance of the queries. Option D: Using a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library and exporting the data as CSV files would require more steps and skills than using BigQuery and Vertex AI. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. Pandas is a popular Python library that can manipulate and analyze data in a tabular format. However, using Vertex AI Workbench and pandas would require creating a notebook instance, writing Python code, installing and importing pandas, connecting to BigQuery, loading and preprocessing the data, and exporting the data as CSV files. Moreover, pandas can have limitations such as memory usage, scalability, and compatibility, which can affect the efficiency and reliability of the data processing. Reference: Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.3 Training models by using AutoML Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Lowcode ML Solutions, Section 4.3: AutoML BigQuery Vertex AI Dataflow TFRecord CSV Vertex AI Workbench Pandas

Answer: B
Explanation: The best option for reducing the sensitivity of the dataset before training the model is to use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption. This option allows you to keep every column in the dataset, while protecting the sensitive data from unauthorized access or exposure. The Cloud DLP API can detect and classify various types of sensitive data, such as names, email addresses, phone numbers, credit card numbers, and more1. Dataflow can create scalable and reliable pipelines to process large volumes of data from BigQuery and other sources2. Format Preserving Encryption (FPE) is a technique that encrypts sensitive data while preserving its original format and length, which can help maintain the utility and validity of the data3. By using Dataflow with the DLP API, you can apply FPE to the sensitive values in the dataset, and store the encrypted data in BigQuery or another destination. You can also use the same pipeline to decrypt the data when needed, by using the same encryption key and method4. The other options are not as suitable as option B, for the following reasons: Option A: Using Dataflow to ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column, would reduce the sensitivity of the data, but also the utility and accuracy of the data. Randomization is a technique that replaces sensitive data with random values, which can prevent re-identification of the data, but also distort the distribution and relationships of the data3. This can affect the performance and quality of the ML model, especially if every column is critical to the model. Option C: Using the Cloud DLP API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt, would reduce the sensitivity of the data, but also the utility and validity of the data. AES-256 is a symmetric encryption algorithm that uses a 256-bit key to encrypt and decrypt data. A salt is a random value that is added to the data before encryption, to increase the randomness and security of the encrypted data. However, AES-256 does not preserve the format or length of the original data, which can cause problems when storing or processing the data. For example, if the original data is a 10-digit phone number, AES-256 would produce a much longer and different string, which can break the schema or logic of the dataset3. Option D: Before training, using BigQuery to select only the columns that do not contain sensitive data, and creating an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals, would reduce the exposure of the sensitive data, but also the completeness and relevance of the data. An authorized view is a BigQuery view that allows you to share query results with particular users or groups, without giving them access to the underlying tables. However, this option assumes that you can identify the columns that do not contain sensitive data, which may not be easy or accurate. Moreover, this option would remove some columns from the dataset, which can affect the performance and quality of the ML model, especially if every column is critical to the model. Reference: Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week 2: Privacy Google Cloud Professional Machine Learning Engineer Exam Guide, Section 5: Developing responsible AI solutions, 5.2 Implementing privacy techniques Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 9: Responsible AI, Section 9.4: Privacy De-identification techniques Cloud Data Loss Prevention (DLP) API Dataflow Using Dataflow and Sensitive Data Protection to securely tokenize and import data from a relational database to BigQuery [AES encryption] [Salt (cryptography)] [Authorized views]

Answer: D
Explanation: Quantization is a technique that reduces the numerical precision of the weights and activations of a neural network, which can improve the inference speed and reduce the memory footprint of the model1. Reducing the floating point precision from tf.float64 to tf.float16 can potentially halve the latency and memory usage of the model, while having minimal impact on the accuracy2. Increasing the dropout rate to 0.8 in either mode would not affect the latency, but would likely degrade the performance of the model significantly, as dropout is a regularization technique that randomly drops out units during training to prevent overfitting3. Switching from CPU to GPU serving may or may not improve the latency, depending on the hardware specifications and the model complexity, but it would also incur additional costs and complexity for deployment4

Answer: D

Explanation:

The best option for automating the model retraining workflow is to use GitHub Actions and Cloud

Build. GitHub Actions is a service that can create and run workflows for continuous integration and

continuous delivery (CI/CD) on GitHub. GitHub Actions can run tests, build and deploy code, and

trigger other actions based on events such as code changes, pull requests, or manual triggers. Cloud

Build is a service that can create and run scalable and reliable pipelines to build, test, and deploy

software on Google Cloud. Cloud Build can build custom Docker images, push the images to Artifact

Registry, and launch the pipeline in Vertex AI Pipelines. Vertex AI Pipelines is a service that can

orchestrate machine learning (ML) workflows using Vertex AI. Vertex AI Pipelines can run

preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the

ML model. By using GitHub Actions and Cloud Build, users can leverage the power and flexibility of

Google Cloud to automate the model retraining workflow, while minimizing the steps required to

build the workflow.

The other options are not as good as option D, for the following reasons:

Option A: Triggering a Cloud Build workflow to run tests, build custom Docker images, push the

images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines would require more

configuration and maintenance than using GitHub Actions and Cloud Build. Cloud Build is a service

that can create and run pipelines to build, test, and deploy software on Google Cloud, but it is not

designed to integrate with GitHub or other source code repositories. To trigger a Cloud Build

workflow from GitHub, users would need to set up a webhook, a Cloud Pub/Sub topic, and a Cloud

Function1. Moreover, Cloud Build does not support manual triggers, which limits the flexibility of the

workflow2.

Option B: Triggering GitHub Actions to run the tests, launching a job on Cloud Run to build custom

Docker images, pushing the images to Artifact Registry, and launching the pipeline in Vertex AI

Pipelines would require more steps and resources than using GitHub Actions and Cloud Build. Cloud

Run is a service that can run stateless containers on a fully managed environment or on Anthos.

Cloud Run can build custom Docker images, but it is not optimized for this task. Users would need to

write a Dockerfile, a cloudbuild.yaml file, and a Cloud Run service configuration file, and use the

gcloud command-line tool to build and deploy the image3. Moreover, Cloud Run is designed for

serving HTTP requests, not for running ML pipelines, which can have different performance and

scalability requirements.

Option C: Triggering GitHub Actions to run the tests, building custom Docker images, pushing the

images to Artifact Registry, and launching the pipeline in Vertex AI Pipelines would require more

skills and tools than using GitHub Actions and Cloud Build. GitHub Actions can run tests and build

code, but it is not specialized for building Docker images. Users would need to install and configure

Docker on the GitHub Actions runner, write a Dockerfile, and use the docker command-line tool to

build and push the image. Moreover, GitHub Actions has limitations on the disk space, memory, and

CPU of the runner, which can affect the speed and reliability of the image building process.

Reference:

Building CI/CD for Vertex AI pipelines: The first solution

Cloud Build

GitHub Actions

Vertex AI Pipelines

Triggering builds from GitHub

Triggering builds manually

Building containers

Cloud Run

Answer: C
Explanation: The best option for analyzing large and complex datasets while minimizing computational resources is to use a combination of BigQuery and Vertex AI Workbench. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on petabytes of data. BigQuery can calculate descriptive statistics such as mean, median, and mode by using SQL functions such as AVG, PERCENTILE_CONT, and MODE. Vertex AI Workbench is a managed service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data visualization and statistical analysis. Vertex AI Workbench can connect to BigQuery and use the results of the queries to create time plots and run statistical tests for hypothesis testing. By using BigQuery and Vertex AI Workbench, users can leverage the power and flexibility of Google Cloud to perform exploratory data analysis on large and complex datasets. Reference: Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML

Answer: A
Explanation: The problem of the text classifier is that it has a high false positive rate for comments that reference certain underrepresented religious groups. This means that the classifier is not able to distinguish between toxic and non-toxic language when those groups are mentioned. One possible reason for this is that the training data does not have enough examples of non-toxic comments that reference those groups, leading to a biased model. Therefore, a possible solution is to add synthetic training data where those phrases are used in non-toxic ways, which can help the model learn to generalize better and reduce the false positive rate. Synthetic data is artificially generated data that mimics the characteristics of real data, and can be used to augment the existing data when the real data is scarce or imbalanced. Reference: Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week 3: Fairness Google Cloud Professional Machine Learning Engineer Exam Guide, Section 4: Ensuring solution quality, 4.4 Evaluating fairness and bias in ML models Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 9: Responsible AI, Section 9.3: Fairness and Bias

Answer: A

Explanation:

The best option for building an ML model to predict customer purchase behavior in BigQuery ML is to

use the transform clause with the ML.ONE_HOT_ENCODER function on the categorical features at

model creation and select the categorical and non-categorical features. This option allows you to

encode the categorical features as one-hot vectors, which are binary vectors that have only one nonzero

element. One-hot encoding is a common technique for handling categorical features in ML

models, as it can reduce the dimensionality and sparsity of the data, and avoid the ordinality

problem that arises when using numerical labels for categorical values1. The transform clause is a

feature of BigQuery ML that lets you apply SQL expressions to transform the input data at model

creation time. The transform clause can perform feature engineering, such as one-hot encoding, on

the fly, without requiring you to create and store a new table with the transformed data2. By using

the transform clause with the ML.ONE_HOT_ENCODER function, you can create and train an ML

model in BigQuery ML with a single SQL statement, and export it to Cloud Storage for online

prediction.

The other options are not as good as option A, for the following reasons:

Option B: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the

encoded categorical features and non-categorical features as inputs to create your model, would

require more steps and storage than using the transform clause. The ML.ONE_HOT_ENCODER

function is a BigQuery ML function that returns a one-hot encoded vector for a given categorical

value. However, using this function alone would not apply the one-hot encoding to the input data at

model creation time. You would need to create a new table with the encoded features, and use that

table as the input to create your model. This would incur additional storage costs and reduce the

performance of the queries.

Option C: Using the create model statement and selecting the categorical and non-categorical

features, would not handle the categorical features properly and could result in a poor model

performance. The create model statement is a BigQuery ML statement that creates and trains an ML

model from a SQL query. However, if the input data contains categorical features, you need to

encode them as one-hot vectors or use the category_count option to specify the number of

categories for each feature. Otherwise, BigQuery ML would treat the categorical features as

numerical values, which can introduce bias and noise into the model3.

Option D: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the

encoded categorical features and non-categorical features as inputs to create your model, is the

same as option B, and has the same drawbacks.

Reference:

Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for

ML on Google Cloud, Week 2: Feature Engineering

Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code

ML solutions, 1.1 Developing ML models by using BigQuery ML

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data

Engineering for ML, Section 3.2: BigQuery for ML

One-hot encoding

Using the TRANSFORM clause for feature engineering

Creating a model

ML.ONE_HOT_ENCODER function

Answer: C
Explanation: The best option for choosing a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by the model address an imminent machine failure is to choose the model with the highest recall where precision is greater than 0.5. This option has the following advantages: It maximizes the recall, which is the proportion of actual failures that are correctly predicted by the model. Recall is also known as sensitivity or true positive rate (TPR), and it is calculated as: mathrmRecall=fracmathrmTPmathrmTP+mathrmFN where TP is the number of true positives (actual failures that are predicted as failures) and FN is the number of false negatives (actual failures that are predicted as non-failures). By maximizing the recall, the model can reduce the number of false negatives, which are the most costly and undesirable outcomes for the predictive maintenance use case, as they represent missed failures that can lead to machine breakdown and downtime. It constrains the precision, which is the proportion of predicted failures that are actual failures. Precision is also known as positive predictive value (PPV), and it is calculated as: mathrmPrecision=fracmathrmTPmathrmTP+mathrmFP where FP is the number of false positives (actual non-failures that are predicted as failures). By constraining the precision to be greater than 0.5, the model can ensure that more than 50% of the maintenance jobs triggered by the model address an imminent machine failure, which can avoid unnecessary or wasteful maintenance costs. The other options are less optimal for the following reasons: Option A: Choosing the model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5 may not prioritize detection, as the AUC ROC does not directly measure the recall. The AUC ROC is a summary metric that evaluates the overall performance of a binary classifier across all possible thresholds. The ROC curve plots the TPR (recall) against the false positive rate (FPR), which is the proportion of actual non-failures that are incorrectly predicted by the model. The AUC ROC is the area under the ROC curve, and it ranges from 0 to 1, where 1 represents a perfect classifier. However, choosing the model with the highest AUC ROC may not maximize the recall, as the AUC ROC is influenced by both the TPR and the FPR, and it does not account for the precision or the specificity (the proportion of actual non-failures that are correctly predicted by the model). Option B: Choosing the model with the lowest root mean squared error (RMSE) and recall greater than 0.5 may not prioritize detection, as the RMSE is not a suitable metric for binary classification. The RMSE is a regression metric that measures the average magnitude of the error between the predicted and the actual values. The RMSE is calculated as: mathrmRMSE=sqrtfrac1nsumi=1n (yi âˆ’hatyi )2 where yi is the actual value, hatyi is the predicted value, and n is the number of observations. However, choosing the model with the lowest RMSE may not optimize the detection of failures, as the RMSE is sensitive to outliers and does not account for the class imbalance or the cost of misclassification. Option D: Choosing the model with the highest precision where recall is greater than 0.5 may not prioritize detection, as the precision may not be the most important metric for the predictive maintenance use case. The precision measures the accuracy of the positive predictions, but it does not reflect the sensitivity or the coverage of the model. By choosing the model with the highest precision, the model may sacrifice the recall, which is the proportion of actual failures that are correctly predicted by the model. This may increase the number of false negatives, which are the most costly and undesirable outcomes for the predictive maintenance use case, as they represent missed failures that can lead to machine breakdown and downtime. Reference: Evaluation Metrics (Classifiers) - Stanford University Evaluation of binary classifiers - Wikipedia Predictive Maintenance: The greatest benefits and smart use cases

Answer: C

Explanation:

The best option for developing an image classification model by using a large dataset that contains

labeled images in a Cloud Storage bucket is to import the labeled images as a managed dataset in

Vertex AI and use AutoML to train the model. This option allows you to leverage the power and

simplicity of Google Cloud to create and deploy a high-quality image classification model with

minimal code and configuration. Vertex AI is a unified platform for building and deploying machine

learning solutions on Google Cloud. Vertex AI can create a managed dataset from a Cloud Storage

bucket that contains labeled images, which can be used to train an AutoML model. AutoML is a

service that can automatically build and optimize machine learning models for various tasks, such as

image classification, object detection, natural language processing, and tabular data analysis.

AutoML can handle the complex aspects of machine learning, such as feature engineering, model

architecture, hyperparameter tuning, and model evaluation. AutoML can also evaluate, deploy, and

monitor the image classification model, and provide online or batch predictions. By using Vertex AI

and AutoML, users can develop an image classification model by using a large dataset with ease and

efficiency.

The other options are not as good as option C, for the following reasons:

Option A: Using Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads

the images from Cloud Storage and trains the model would require more skills and steps than using

Vertex AI and AutoML. Vertex AI Pipelines is a service that can orchestrate machine learning

workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom

Docker images, and evaluate, deploy, and monitor the machine learning model. Kubeflow Pipelines

SDK is a Python library that can create and run pipelines on Vertex AI Pipelines or on Kubeflow, an

open-source platform for machine learning on Kubernetes. However, using Vertex AI Pipelines and

Kubeflow Pipelines SDK would require writing code, building Docker images, defining pipeline

components and steps, and managing the pipeline execution and artifacts. Moreover, Vertex AI

Pipelines and Kubeflow Pipelines SDK are not specialized for image classification, and users would

need to use other libraries or frameworks, such as TensorFlow or PyTorch, to build and train the

image classification model.

Option B: Using Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads

the images from Cloud Storage and trains the model would require more skills and steps than using

Vertex AI and AutoML. TensorFlow Extended (TFX) is a framework that can create and run end-to-end

machine learning pipelines on TensorFlow, a popular library for building and training deep learning

models. TFX can preprocess the data, train and evaluate the model, validate and push the model,

and serve the model for online or batch predictions. However, using Vertex AI Pipelines and TFX

would require writing code, building Docker images, defining pipeline components and steps, and

managing the pipeline execution and artifacts. Moreover, TFX is not optimized for image

classification, and users would need to use other libraries or tools, such as TensorFlow Data

Validation, TensorFlow Transform, and TensorFlow Hub, to handle the image data and the model

architecture.

Option D: Converting the image dataset to a tabular format using Dataflow, loading the data into

BigQuery, and using BigQuery ML to train the model would not handle the image data properly and

could result in a poor model performance. Dataflow is a service that can create scalable and reliable

pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by

using Apache Beam, a programming model for defining and executing data processing workflows.

BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and

interactive queries on large datasets. BigQuery ML is a service that can create and train machine

learning models by using SQL queries on BigQuery. However, converting the image data to a tabular

format would lose the spatial and semantic information of the images, which are essential for image

classification. Moreover, BigQuery ML is not specialized for image classification, and users would

need to use other tools or techniques, such as feature hashing, embedding, or one-hot encoding, to

handle the categorical features.

Answer: B
Explanation: The best option for scaling the training workload while minimizing cost is to package the code with Setuptools, and use a pre-built container. Train the model with Vertex AI using a custom tier that contains the required GPUs. This option has the following advantages: It allows the code to be easily packaged and deployed, as Setuptools is a Python tool that helps to create and distribute Python packages, and pre-built containers are Docker images that contain all the dependencies and libraries needed to run the code. By packaging the code with Setuptools, and using a pre-built container, you can avoid the hassle and complexity of building and maintaining your own custom container, and ensure the compatibility and portability of your code across different environments. It leverages the scalability and performance of Vertex AI, which is a fully managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. By training the model with Vertex AI, you can take advantage of the distributed and parallel training capabilities of Vertex AI, which can speed up the training process and improve the model quality. Vertex AI also supports various frameworks and models, such as PyTorch and ResNet50, and allows you to use custom containers and custom tiers to customize your training configuration and resources. It reduces the cost and complexity of the training process, as Vertex AI allows you to use a custom tier that contains the required GPUs, which can optimize the resource utilization and allocation for your training job. By using a custom tier that contains 4 V100 GPUs, you can match the number and type of GPUs that you plan to use for your training job, and avoid paying for unnecessary or underutilized resources. Vertex AI also offers various pricing options and discounts, such as persecond billing, sustained use discounts, and preemptible VMs, that can lower the cost of the training process. The other options are less optimal for the following reasons: Option A: Configuring a Compute Engine VM with all the dependencies that launches the training. Train the model with Vertex AI using a custom tier that contains the required GPUs, introduces additional complexity and overhead. This option requires creating and managing a Compute Engine VM, which is a virtual machine that runs on Google Cloud. However, using a Compute Engine VM to launch the training may not be necessary or efficient, as it requires installing and configuring all the dependencies and libraries needed to run the code, and maintaining and updating the VM. Moreover, using a Compute Engine VM to launch the training may incur additional cost and latency, as it requires paying for the VM usage and transferring the data and the code between the VM and Vertex AI. Option C: Creating a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and using it to train the model, introduces additional cost and risk. This option requires creating and managing a Vertex AI Workbench user-managed notebooks instance, which is a service that allows you to create and run Jupyter notebooks on Google Cloud. However, using a Vertex AI Workbench user-managed notebooks instance to train the model may not be optimal or secure, as it requires paying for the notebooks instance usage, which can be expensive and wasteful, especially if the notebooks instance is not used for other purposes. Moreover, using a Vertex AI Workbench usermanaged notebooks instance to train the model may expose the model and the data to potential security or privacy issues, as the notebooks instance is not fully managed by Google Cloud, and may be accessed or modified by unauthorized users or malicious actors. Option D: Creating a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs. Prepare and submit a TFJob operator to this node pool, introduces additional complexity and cost. This option requires creating and managing a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires creating and managing a node pool that has 4 V100 GPUs, which is a group of nodes that share the same configuration and resources. Furthermore, this option requires preparing and submitting a TFJob operator to this node pool, which is a Kubernetes custom resource that defines a TensorFlow training job. However, using Google Kubernetes Engine, node pool, and TFJob operator to train the model may not be necessary or efficient, as it requires configuring and maintaining the cluster, the node pool, and the TFJob operator, and paying for their usage. Moreover, using Google Kubernetes Engine, node pool, and TFJob operator to train the model may not be compatible or scalable, as they are designed for TensorFlow models, not PyTorch models, and may not support distributed or parallel training. Reference: [Vertex AI: Training with custom containers] [Vertex AI: Using custom machine types] [Setuptools documentation] [PyTorch documentation] [ResNet50 | PyTorch]

Answer: B, C

Explanation:

The best options for adjusting the training parameters in AutoML to improve model performance are

to decrease the score threshold and add more positive examples to the training set. These options

can help increase the detection rate of fraudulent transactions, which is the priority for this use case.

The score threshold is a parameter that determines the minimum probability score that a prediction

must have to be classified as positive. Decreasing the score threshold can increase the recall of the

model, which is the proportion of actual positive cases that are correctly identified. Increasing the

recall can help reduce the number of false negatives, which are fraudulent transactions that are

missed by the model. However, decreasing the score threshold can also decrease the precision of the

model, which is the proportion of positive predictions that are actually correct. Decreasing the

precision can increase the number of false positives, which are legitimate transactions that are

flagged as fraudulent by the model. Therefore, there is a trade-off between recall and precision, and

the optimal score threshold depends on the business objective and the cost of errors1. Adding more

positive examples to the training set can help balance the data distribution and improve the model

performance. Positive examples are the instances that belong to the target class, which in this case

are fraudulent transactions. Negative examples are the instances that belong to the other class,

which in this case are legitimate transactions. Fraudulent transactions are usually rare and

imbalanced compared to legitimate transactions, which can cause the model to be biased towards

the majority class and fail to learn the characteristics of the minority class. Adding more positive

examples can help the model learn more features and patterns of the fraudulent transactions, and

increase the detection rate2.

The other options are not as good as options B and C, for the following reasons:

Option A: Increasing the score threshold would decrease the detection rate of fraudulent

transactions, which is the opposite of the desired outcome. Increasing the score threshold would

decrease the recall of the model, which is the proportion of actual positive cases that are correctly

identified. Decreasing the recall would increase the number of false negatives, which are fraudulent

transactions that are missed by the model. Increasing the score threshold would increase the

precision of the model, which is the proportion of positive predictions that are actually correct.

Increasing the precision would decrease the number of false positives, which are legitimate

transactions that are flagged as fraudulent by the model. However, in this use case, the cost of false

negatives is much higher than the cost of false positives, so increasing the score threshold is not a

good option1.

Option D: Adding more negative examples to the training set would not improve the model

performance, and could worsen the data imbalance. Negative examples are the instances that

belong to the other class, which in this case are legitimate transactions. Legitimate transactions are

usually abundant and dominant compared to fraudulent transactions, which can cause the model to

be biased towards the majority class and fail to learn the characteristics of the minority class. Adding

more negative examples would exacerbate this problem, and decrease the detection rate of the

fraudulent transactions2.

Option E: Reducing the maximum number of node hours for training would not improve the model

performance, and could limit the model optimization. Node hours are the units of computation that

are used to train an AutoML model. The maximum number of node hours is a parameter that

determines the upper limit of node hours that can be used for training. Reducing the maximum

number of node hours would reduce the training time and cost, but also the model quality and

accuracy. Reducing the maximum number of node hours would limit the number of iterations, trials,

and evaluations that the model can perform, and prevent the model from finding the optimal

hyperparameters and architecture3.

Reference:

Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI,

Week 4: Evaluation

Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Developing highquality

ML models, 2.2 Handling imbalanced data

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Lowcode

ML Solutions, Section 4.3: AutoML

Understanding the score threshold slider

Handling imbalanced data sets in machine learning

AutoML Vision pricing

Answer: C
Explanation: The best option for dealing with the missing categorical variable in the test set is to apply one-hot encoding on the categorical variables in the test data. This option has the following advantages: It ensures the consistency and compatibility of the data format for the ML model, as the one-hot encoding transforms the categorical variables into binary vectors that can be easily processed by the model. By applying one-hot encoding on the categorical variables in the test data, you can match the number and order of the features in the test data with the training data, and avoid any errors or discrepancies in the model prediction. It preserves the information and relevance of the data for the ML model, as the one-hot encoding creates a separate feature for each possible value of the categorical variable, and assigns a value of 1 to the feature corresponding to the actual value of the variable, and 0 to the rest. By applying onehot encoding on the categorical variables in the test data, you can retain the original meaning and importance of the categorical variable, and avoid any loss or distortion of the data. The other options are less optimal for the following reasons: Option A: Randomly redistributing the data, with 70% for the training set and 30% for the test set, introduces additional complexity and risk. This option requires reshuffling and splitting the data again, which can be tedious and time-consuming. Moreover, this option may not guarantee that the missing categorical variable will be present in the test set, as it depends on the randomness of the data distribution. Furthermore, this option may affect the quality and validity of the ML model, as it may change the data characteristics and patterns that the model has learned from the original training set. Option B: Using sparse representation in the test set introduces additional overhead and inefficiency. This option requires converting the categorical variables in the test set into sparse vectors, which are vectors that have mostly zero values and only store the indices and values of the non-zero elements. However, using sparse representation in the test set may not be compatible with the ML model, as the model expects the input data to have the same format and dimensionality as the training data, which uses one-hot encoding. Moreover, using sparse representation in the test set may not be efficient or scalable, as it requires additional computation and memory to store and process the sparse vectors. Option D: Collecting more data representing all categories introduces additional cost and delay. This option requires obtaining and labeling more data that contains the missing categorical variable, which can be expensive and time-consuming. Moreover, this option may not be feasible or necessary, as the missing categorical variable may not be available or relevant for the test data, depending on the data source or the business problem.

Answer: C
Explanation: The best option for parametrizing the model training in Kubeflow Pipelines is to add a ContainerOp to the pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage. This option has the following advantages: It allows the data transformation to be performed as part of the Kubeflow Pipeline, which can ensure the consistency and reproducibility of the data processing and the model training. By adding a ContainerOp to the pipeline, you can define the parameters and the logic of the data transformation step, and integrate it with the other steps of the pipeline, such as the model training and evaluation. It leverages the scalability and performance of Dataproc, which is a fully managed service that runs Apache Spark and Apache Hadoop clusters on Google Cloud. By spinning a Dataproc cluster, you can run the PySpark transformation on the Parquet files stored in the Hive table, and take advantage of the parallelism and speed of Spark. Dataproc also supports various features and integrations, such as autoscaling, preemptible VMs, and connectors to other Google Cloud services, that can optimize the data processing and reduce the cost. It simplifies the data storage and access, as the transformed data is saved in Cloud Storage, which is a scalable, durable, and secure object storage service. By saving the transformed data in Cloud Storage, you can avoid the overhead and complexity of managing the data in the Hive table or the Parquet files. Moreover, you can easily access the transformed data from Cloud Storage, using various tools and frameworks, such as TensorFlow, BigQuery, or Vertex AI. The other options are less optimal for the following reasons: Option A: Removing the data transformation step from the pipeline eliminates the parametrization of the model training, as the data processing and the model training are decoupled and independent. This option requires running the PySpark transformation separately from the Kubeflow Pipeline, which can introduce inconsistency and unreproducibility in the data processing and the model training. Moreover, this option requires managing the data in the Hive table or the Parquet files, which can be cumbersome and inefficient. Option B: Containerizing the PySpark transformation step, and adding it to the pipeline introduces additional complexity and overhead. This option requires creating and maintaining a Docker image that can run the PySpark transformation, which can be challenging and time-consuming. Moreover, this option requires running the PySpark transformation on a single container, which can be slow and inefficient, as it does not leverage the parallelism and performance of Spark. Option D: Deploying Apache Spark at a separate node pool in a Google Kubernetes Engine cluster, and adding a ContainerOp to the pipeline that invokes a corresponding transformation job for this Spark instance introduces additional complexity and cost. This option requires creating and managing a separate node pool in a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires deploying and running Apache Spark on the node pool, which can be tedious and costly, as it requires configuring and maintaining the Spark cluster, and paying for the node pool usage.

Answer: B
Explanation: This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons: It indicates that the model is not learning from the data, but rather memorizing the majority class. Since 90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the models accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription). It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the models accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the models accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them. Reference: How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery

Answer: C
Explanation: The best option for protecting sensitive customer data that might be used in the ML models is to coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGITUDE into single precision. This option has the following advantages: It preserves the utility and relevance of the data for the ML models, as the coarsened data still captures the essential information and patterns that the models need to learn. For example, putting AGE into quantiles can group the customers into different age ranges, which can be useful for predicting their preferences or behavior. Rounding LATITUDE_LONGITUDE into single precision can reduce the precision of the location data, but still retain the general geographic region of the customers, which can be useful for personalizing the recommendations or offers. It reduces the risk of exposing the personal or private information of the customers, as the coarsened data makes it harder to identify or re-identify the individual customers from the data. For example, putting AGE into quantiles can hide the exact age of the customers, which can be considered sensitive or confidential. Rounding LATITUDE_LONGITUDE into single precision can obscure the exact location of the customers, which can be considered sensitive or confidential. The other options are less optimal for the following reasons: Option A: Tokenizing all of the fields using hashed dummy values to replace the real values eliminates the utility and relevance of the data for the ML models, as the tokenized data loses all the information and patterns that the models need to learn. For example, tokenizing AGE using hashed dummy values can make the data meaningless and irrelevant, as the models cannot learn anything from the random tokens. Tokenizing LATITUDE_LONGITUDE using hashed dummy values can make the data meaningless and irrelevant, as the models cannot learn anything from the random tokens. Option B: Using principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector reduces the utility and relevance of the data for the ML models, as the PCA vector may not capture all the information and patterns that the models need to learn. For example, using PCA to reduce AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE to one PCA vector can lose some information or introduce noise in the data, as the PCA vector is a linear combination of the original features, which may not reflect their true relationship or importance. Moreover, using PCA to reduce the four sensitive fields to one PCA vector may not reduce the risk of exposing the personal or private information of the customers, as the PCA vector may still be reversible or linkable to the original data, depending on the amount of variance explained by the PCA vector and the availability of the PCA transformation matrix. Option D: Removing all sensitive data fields, and asking the data science team to build their models using non-sensitive data reduces the utility and relevance of the data for the ML models, as the nonsensitive data may not contain enough information and patterns that the models need to learn. For example, removing AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE from the data can make the data insufficient and unrepresentative, as the models may not be able to learn the factors that influence the customers preferences or behavior. Moreover, removing all sensitive data fields from the data may not be necessary or feasible, as the data protection legislation may allow the use of sensitive data for the ML models, as long as the data is processed in a secure and ethical manner, and the customers consent and rights are respected. Reference: Protecting Sensitive Data and AI Models with Confidential Computing | NVIDIA Technical Blog Training machine learning models from sensitive data | Fast Data Science Securing ML applications. Model security and protection - Medium Security of AI/ML systems, ML model security | Cossack Labs Vulnerabilities, security and privacy for machine learning models

Answer: B
Explanation: The simplest way to deploy a logistic regression model with BigQuery ML to production while adding minimal latency is to export the model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline. This option has the following advantages: It allows the model prediction to be performed in real time, as part of the Dataflow streaming pipeline that processes the ticket purchase requests. This ensures that the promo code offer is based on the most recent data and customer behavior, and that the offer is delivered to the customer without delay. It leverages the compatibility and performance of TensorFlow and Dataflow, which are both part of the Google Cloud ecosystem. TensorFlow is a popular and powerful framework for building and deploying machine learning models, and Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. By using the tfx_bsl.public.beam.RunInference step, you can easily integrate your TensorFlow model with your Dataflow pipeline, and take advantage of the parallelism and scalability of Dataflow. It simplifies the model deployment and management, as the model is packaged with the Dataflow pipeline and does not require a separate service or endpoint. The model can be updated by redeploying the Dataflow pipeline with a new model version. The other options are less optimal for the following reasons: Option A: Running batch inference with BigQuery ML every five minutes on each new set of tickets issued introduces additional latency and complexity. This option requires running a separate BigQuery job every five minutes, which can incur network overhead and latency. Moreover, this option requires storing and retrieving the intermediate results of the batch inference, which can consume storage space and increase the data transfer time. Option C: Exporting the model in TensorFlow format, deploying it on Vertex AI, and querying the prediction endpoint from the streaming pipeline introduces additional latency and cost. This option requires creating and managing a Vertex AI endpoint, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, querying the Vertex AI endpoint from the streaming pipeline requires making an HTTP request, which can incur network overhead and latency. Moreover, this option requires paying for the Vertex AI endpoint usage, which can increase the cost of the model deployment. Option D: Converting the model with TensorFlow Lite (TFLite), and adding it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub introduces additional challenges and risks. This option requires converting the model to a TFLite format, which is a lightweight and optimized format for running TensorFlow models on mobile and embedded devices. However, converting the model to TFLite may not preserve the accuracy or functionality of the original model, as some operations or features may not be supported by TFLite. Moreover, this option requires updating the mobile app with the TFLite model, which can be tedious and timeconsuming, and may depend on the users willingness to update the app. Additionally, this option may expose the model to potential security or privacy issues, as the model is running on the users device and may be accessed or modified by malicious actors. Reference: [Exporting models for prediction | BigQuery ML] [tfx_bsl.public.beam.run_inference | TensorFlow Extended] [Vertex AI documentation] [TensorFlow Lite documentation]

Answer: D
Explanation: The best option for determining how often to retrain your model to maintain a high level of performance while minimizing cost is to run training-serving skew detection batch jobs every few days. Training-serving skew refers to the discrepancy between the distributions of the features in the training dataset and the serving data. This can cause the model to perform poorly on the new data, as it is not representative of the data that the model was trained on. By running training-serving skew detection batch jobs, you can monitor the changes in the feature distributions over time, and identify when the skew becomes significant enough to affect the model performance. If skew is detected, you can send the most recent serving data to the labeling service, and use the labeled data to retrain your model. This option has the following benefits: It allows you to retrain your model only when necessary, based on the actual data changes, rather than on a fixed schedule or a heuristic. This can save you the cost of the labeling service and the retraining process, and also avoid overfitting or underfitting your model. It leverages the existing tools and frameworks for training-serving skew detection, such as TensorFlow Data Validation (TFDV) and Vertex Data Labeling. TFDV is a library that can compute and visualize descriptive statistics for your datasets, and compare the statistics across different datasets. Vertex Data Labeling is a service that can label your data with high quality and low latency, using either human labelers or automated labelers. It integrates well with the MLOps practices, such as continuous integration and continuous delivery (CI/CD), which can automate the workflow of running the skew detection jobs, sending the data to the labeling service, retraining the model, and deploying the new model version. The other options are less optimal for the following reasons: Option A: Training an anomaly detection model on the training dataset, and running all incoming requests through this model, introduces additional complexity and overhead. This option requires building and maintaining a separate model for anomaly detection, which can be challenging and time-consuming. Moreover, this option requires running the anomaly detection model on every request, which can increase the latency and resource consumption of the prediction service. Additionally, this option may not capture the subtle changes in the feature distributions that can affect the model performance, as anomalies are usually defined as rare or extreme events. Option B: Identifying temporal patterns in your models performance over the previous year, and creating a schedule for sending serving data to the labeling service for the next year, introduces additional assumptions and risks. This option requires analyzing the historical data and model performance, and finding the patterns that can explain the variations in the model performance over time. However, this can be difficult and unreliable, as the patterns may not be consistent or predictable, and may depend on various factors that are not captured by the data. Moreover, this option requires creating a schedule based on the past patterns, which may not reflect the future changes in the data or the environment. This can lead to either sending too much or too little data to the labeling service, resulting in either wasted cost or degraded performance. Option C: Comparing the cost of the labeling service with the lost revenue due to model performance degradation over the past year, and adjusting the frequency of model retraining accordingly, introduces additional challenges and trade-offs. This option requires estimating the cost of the labeling service and the lost revenue due to model performance degradation, which can be difficult and inaccurate, as they may depend on various factors that are not easily quantifiable or measurable. Moreover, this option requires finding the optimal balance between the cost and the performance, which can be subjective and variable, as different stakeholders may have different preferences and expectations. Furthermore, this option may not account for the potential impact of the model performance degradation on other aspects of the business, such as customer satisfaction, retention, or loyalty.

Answer: C
Explanation: The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages: It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline. It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost. It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuerys built-in scheduling feature. You can also set up alerts or notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly. The other options are less optimal for the following reasons: Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring. Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost. Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations. Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost. Reference: [BigQuery documentation] [Vertex AI Model Monitoring documentation] [Cloud Tasks documentation] [Cloud Composer documentation] [Dataflow documentation]

Answer: B

Explanation:

The best option for deploying a scikit-learn classification model to production is to deploy an online

Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to

leverage the power and scalability of Google Cloud to serve requests 24 and handle millions of

requests per second. Vertex AI is a unified platform for building and deploying machine learning

solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction

endpoint, which can provide low-latency predictions for individual instances. An online prediction

endpoint consists of one or more replicas, which are copies of the model that run on virtual

machines. The max replica count is a parameter that determines the maximum number of replicas

that can be created for the endpoint. By setting the max replica count to 100, you can enable the

endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when

the traffic decreases. This can help minimize the cost of deployment, as you only pay for the

resources that you use. Moreover, you can use the autoscaling algorithm option to optimize the

scaling behavior of the endpoint based on the latency and utilization metrics1.

The other options are not as good as option B, for the following reasons:

Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1

would not be able to serve requests 24 and handle millions of requests per second. Setting the

max replica count to 1 would limit the endpoint to only one replica, which can cause performance

issues and service disruptions when the traffic increases. Moreover, setting the max replica count to

1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which

can increase the cost of deployment, as you pay for the resources that you do not use1.

Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the

max replica count to 1 would not be able to serve requests 24 and handle millions of requests per

second, and would increase the cost of deployment. Adding a GPU to each replica would increase the

computational power of the endpoint, but it would also increase the cost of deployment, as GPUs

are more expensive than CPUs. Moreover, setting the max replica count to 1 would limit the

endpoint to only one replica, which can cause performance issues and service disruptions when the

traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic

decreases1. Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not

optimized for GPU acceleration2.

Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the

max replica count to 100 would be able to serve requests 24 and handle millions of requests per

second, but it would increase the cost of deployment. Adding a GPU to each replica would increase

the computational power of the endpoint, but it would also increase the cost of deployment, as

GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the

endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when

the traffic decreases, which can help minimize the cost of deployment. However, scikit-learn models

do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration2. Therefore, using

GPUs for scikit-learn models would be unnecessary and wasteful.

Reference:

Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML

Systems, Week 2: Serving ML Predictions

Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in

production, 3.1 Deploying ML models to production

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:

Production ML Systems, Section 6.2: Serving ML Predictions

Online prediction

Scaling online prediction

scikit-learn FAQ

Answer: A

Explanation:

A TPU VM is a virtual machine that has direct access to a Cloud TPU device. TPU VMs provide a

simpler and more flexible way to use Cloud TPUs, as they eliminate the need for a separate host VM

and network setup. TPU VMs also support interactive debugging tools such as TensorFlow Debugger

(tfdbg) and Python Debugger (pdb), which can help researchers develop and troubleshoot complex

models. A v3-8 TPU VM has 8 TPU cores, which can provide high performance and scalability for

training large models. SSHing into the TPU VM allows the user to run and debug the TensorFlow code

directly on the TPU device, without any network overhead or data transfer issues. Reference:

1: TPU VMs Overview

2: TPU VMs Quickstart

3: Debugging TensorFlow Models on Cloud TPUs

Exam Code	Professional-Machine-Learning-Engineer
Exam Name	Google Professional Machine Learning Engineer
Update Date	06 Oct, 2024
Total Questions	270 Questions Answers With Explanation

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