Google Professional Machine Learning Engineer Practice Exams
Last updated on Oct 09,2026- Exam Code: Professional Machine Learning Engineer
- Exam Name: Google Professional Machine Learning Engineer
- Certification Provider: Google
- Latest update: Oct 09,2026
Question #161
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action.
How should you build the model?
- A . Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
- B . Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
- C . Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.
- D . Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question #161
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action.
How should you build the model?
- A . Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
- B . Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
- C . Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.
- D . Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question #161
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action.
How should you build the model?
- A . Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
- B . Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
- C . Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.
- D . Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question #161
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action.
How should you build the model?
- A . Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
- B . Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
- C . Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.
- D . Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “explain the predictions of a trained model”. TensorFlow2 is an open source framework for developing and deploying machine learning and deep learning models. TensorFlow supports various model explainability methods, such as Integrated Gradients3, which is a technique that assigns an importance score to each input feature by approximating the integral of the gradients along the path from a baseline input to the actual input. Integrated Gradients can help explain the output of a deep learning-based model by highlighting the most influential features in the input images. Therefore, option C is the best way to build the model for the given use case. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
TensorFlow
Integrated Gradients
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question #165
You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables.
What should you do?
- A . Create a new view with BigQuery that does not include a column with city information
- B . Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- C . Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
- D . Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
Correct Answer: B
B
Explanation:
One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category. Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise1. One-hot encoding can help linear regression models to capture the effect of different categories on the target variable without imposing any ordinal relationship among them2. Dataprep is a service that allows you to explore,
clean, and transform your data for analysis and machine learning. You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values3. This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.
Reference: One Hot Encoding: A Beginner’s Guide
One-Hot Encoding in Linear Regression Models
Dataprep documentation
B
Explanation:
One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category. Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise1. One-hot encoding can help linear regression models to capture the effect of different categories on the target variable without imposing any ordinal relationship among them2. Dataprep is a service that allows you to explore,
clean, and transform your data for analysis and machine learning. You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values3. This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.
Reference: One Hot Encoding: A Beginner’s Guide
One-Hot Encoding in Linear Regression Models
Dataprep documentation
Question #165
You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables.
What should you do?
- A . Create a new view with BigQuery that does not include a column with city information
- B . Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- C . Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
- D . Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
Correct Answer: B
B
Explanation:
One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category. Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise1. One-hot encoding can help linear regression models to capture the effect of different categories on the target variable without imposing any ordinal relationship among them2. Dataprep is a service that allows you to explore,
clean, and transform your data for analysis and machine learning. You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values3. This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.
Reference: One Hot Encoding: A Beginner’s Guide
One-Hot Encoding in Linear Regression Models
Dataprep documentation
B
Explanation:
One-hot encoding is a technique that converts categorical variables into numerical variables by creating dummy variables for each possible category. Each dummy variable has a value of 1 if the original variable belongs to that category, and 0 otherwise1. One-hot encoding can help linear regression models to capture the effect of different categories on the target variable without imposing any ordinal relationship among them2. Dataprep is a service that allows you to explore,
clean, and transform your data for analysis and machine learning. You can use Dataprep to apply one-hot encoding to your city name variable and make each city a column with binary values3. This way, you can prepare your data using the least amount of coding while maintaining the predictive variables. Therefore, using Dataprep to transform the state column using a one-hot encoding method is the best option for this use case.
Reference: One Hot Encoding: A Beginner’s Guide
One-Hot Encoding in Linear Regression Models
Dataprep documentation
Question #167
You recently created a new Google Cloud Project After testing that you can submit a Vertex Al Pipeline job from the Cloud Shell, you want to use a Vertex Al Workbench user-managed notebook instance to run your code from that instance You created the instance and ran the code but this time the job fails with an insufficient permissions error.
What should you do?
- A . Ensure that the Workbench instance that you created is in the same region of the Vertex Al Pipelines resources you will use.
- B . Ensure that the Vertex Al Workbench instance is on the same subnetwork of the Vertex Al Pipeline resources that you will use.
- C . Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Vertex Al User rote.
- D . Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Notebooks Runner role.
Correct Answer: C
C
Explanation:
Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. Vertex AI Pipelines is a service that allows you to create and manage machine learning workflows using Vertex AI components. To submit a Vertex AI Pipeline job from a Vertex AI Workbench instance, you need to have the appropriate permissions to access the Vertex AI resources. The Identity and Access Management (IAM) Vertex AI User role is a predefined role that grants the minimum permissions required to use Vertex AI services, such as creating and deploying models, endpoints, and pipelines. By assigning the Vertex AI User role to the Vertex AI Workbench instance, you can ensure that the instance has sufficient permissions to submit a Vertex AI Pipeline job. You can assign the role to the instance by using the Cloud Console, the gcloud command-line tool, or the Cloud IAM API.
Reference: The answer can be verified from official Google
Cloud documentation and resources related to Vertex AI Workbench, Vertex AI Pipelines, and IAM.
Vertex AI Workbench | Google Cloud
Vertex AI Pipelines | Google Cloud
Vertex AI roles | Google Cloud
Granting, changing, and revoking access to resources | Google Cloud
C
Explanation:
Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. Vertex AI Pipelines is a service that allows you to create and manage machine learning workflows using Vertex AI components. To submit a Vertex AI Pipeline job from a Vertex AI Workbench instance, you need to have the appropriate permissions to access the Vertex AI resources. The Identity and Access Management (IAM) Vertex AI User role is a predefined role that grants the minimum permissions required to use Vertex AI services, such as creating and deploying models, endpoints, and pipelines. By assigning the Vertex AI User role to the Vertex AI Workbench instance, you can ensure that the instance has sufficient permissions to submit a Vertex AI Pipeline job. You can assign the role to the instance by using the Cloud Console, the gcloud command-line tool, or the Cloud IAM API.
Reference: The answer can be verified from official Google
Cloud documentation and resources related to Vertex AI Workbench, Vertex AI Pipelines, and IAM.
Vertex AI Workbench | Google Cloud
Vertex AI Pipelines | Google Cloud
Vertex AI roles | Google Cloud
Granting, changing, and revoking access to resources | Google Cloud
Question #168
You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort.
What should you do first?
- A . Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.
- B . Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.
- C . Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.
- D . Create a Vertex Al Workbench notebook with instance type n2-standard-4.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Dataproc2 is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it. You can also use Vertex AI Workbench3, a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Dataproc
Vertex AI Workbench
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Dataproc2 is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it. You can also use Vertex AI Workbench3, a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Dataproc
Vertex AI Workbench
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question #168
You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort.
What should you do first?
- A . Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.
- B . Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.
- C . Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.
- D . Create a Vertex Al Workbench notebook with instance type n2-standard-4.
Correct Answer: C
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Dataproc2 is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it. You can also use Vertex AI Workbench3, a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Dataproc
Vertex AI Workbench
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
C
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. Dataproc2 is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it. You can also use Vertex AI Workbench3, a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Dataproc
Vertex AI Workbench
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question #170
You work for an online travel agency that also sells advertising placements on its website to other companies.
You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution.
How should you configure the prediction pipeline?
- A . Embed the client on the website, and then deploy the model on AI Platform Prediction.
- B . Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
- C . Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction.
- D . Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine.
Correct Answer: A
A
Explanation:
In this scenario, the goal is to predict the most relevant web banner that a user should see next on an online travel agency’s website. The model needs to have low latency requirements of 300ms@p99, and there are thousands of web banners to choose from. The exploratory analysis has shown that the navigation context is a good predictor. Security is also important to the company. Given these requirements, the best configuration for the prediction pipeline would be to embed the client on the website and deploy the model on AI Platform Prediction.
Option A is the correct answer.
Option A: Embed the client on the website, and then deploy the model on AI Platform Prediction. This option is the simplest solution that meets the requirements. The client can collect the user’s navigation context and send it to the model deployed on AI Platform Prediction for prediction. AI Platform Prediction can handle large-scale prediction requests and has low latency requirements. This option does not require any additional infrastructure or services, making it the simplest solution.
Option B: Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction. This option adds an additional layer of infrastructure by deploying the gateway on App Engine. While App Engine can handle large-scale requests, it adds complexity to the pipeline and may not be necessary for this use case.
Option C: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction. This option adds even more complexity to the pipeline by deploying the database on Cloud Bigtable. While Cloud Bigtable can provide fast and scalable access to the user’s navigation context, it may not be needed for this use case. Moreover, Cloud Bigtable may introduce additional latency and cost to the pipeline.
Option D: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine. This option is the most complex and costly solution that does not meet the requirements. Deploying the model on Google Kubernetes Engine requires more management and configuration than AI Platform Prediction. Moreover, Google Kubernetes Engine may not be able to meet the low latency requirements of 300ms@p99. Deploying the database on Memorystore also adds unnecessary overhead and cost to the pipeline.
Reference: AI Platform Prediction documentation
App Engine documentation
Cloud Bigtable documentation
[Memorystore documentation]
[Google Kubernetes Engine documentation]
A
Explanation:
In this scenario, the goal is to predict the most relevant web banner that a user should see next on an online travel agency’s website. The model needs to have low latency requirements of 300ms@p99, and there are thousands of web banners to choose from. The exploratory analysis has shown that the navigation context is a good predictor. Security is also important to the company. Given these requirements, the best configuration for the prediction pipeline would be to embed the client on the website and deploy the model on AI Platform Prediction.
Option A is the correct answer.
Option A: Embed the client on the website, and then deploy the model on AI Platform Prediction. This option is the simplest solution that meets the requirements. The client can collect the user’s navigation context and send it to the model deployed on AI Platform Prediction for prediction. AI Platform Prediction can handle large-scale prediction requests and has low latency requirements. This option does not require any additional infrastructure or services, making it the simplest solution.
Option B: Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction. This option adds an additional layer of infrastructure by deploying the gateway on App Engine. While App Engine can handle large-scale requests, it adds complexity to the pipeline and may not be necessary for this use case.
Option C: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction. This option adds even more complexity to the pipeline by deploying the database on Cloud Bigtable. While Cloud Bigtable can provide fast and scalable access to the user’s navigation context, it may not be needed for this use case. Moreover, Cloud Bigtable may introduce additional latency and cost to the pipeline.
Option D: Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine. This option is the most complex and costly solution that does not meet the requirements. Deploying the model on Google Kubernetes Engine requires more management and configuration than AI Platform Prediction. Moreover, Google Kubernetes Engine may not be able to meet the low latency requirements of 300ms@p99. Deploying the database on Memorystore also adds unnecessary overhead and cost to the pipeline.
Reference: AI Platform Prediction documentation
App Engine documentation
Cloud Bigtable documentation
[Memorystore documentation]
[Google Kubernetes Engine documentation]