Google Professional Machine Learning Engineer Exam Practice Test Instant Access

Question 1

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

ACreate a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.

BCreate a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.

CBuild a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.

DUse the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

Answer : B

Cloud Run can be triggered on new data arrivals, which makes it ideal for near-real-time processing. The function then initiates the Vertex AI Pipeline for preprocessing and storing features in Vertex AI Feature Store, aligning with the retraining needs. Cloud Scheduler (Option A) is suitable for scheduled jobs, not event-driven triggers. Dataflow (Option C) is better suited for batch processing or ETL rather than ML preprocessing pipelines.

Question 2

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?

ACreate a Vertex Al pipeline that runs different model training jobs in parallel.

BTrain an AutoML image classification model.

CCreate a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

DCreate a Vertex Al hyperparameter tuning job.

Answer : D

Question 3

You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

AConfigure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

BCreate a Vertex Al experiment, and enable autologging inside the custom job

CUse the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

DRegister each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

Answer : D

Question 4

You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website.

You plan to deploy the model to a new Vertex Al endpoint that will integrate with your website application. You expect higher traffic to the website during

nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?

AConfigure the model deployment settings to use an n1-standard-32 machine type.

BConfigure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.

CConfigure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator. Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.

DConfigure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.

Answer : B

Deploying a model to an endpoint in Vertex AI associates physical resources with the model so it can serve online predictions with low latency1.By configuring the model deployment settings to use an n1-standard-4 machine type and setting the minReplicaCount value to 1 and the maxReplicaCount value to 8, you can ensure that the model scales according to the traffic, thereby minimizing latency and cost1.The n1-standard-4 machine type provides a balance between computing power and cost, and the dynamic scaling allows the model to handle higher traffic during nights and weekends without incurring unnecessary costs during off-peak times

Question 5

You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery,

processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are

writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines.

Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to

avoid additional costs. What should you do?

ADelegate feature engineering to BigQuery and remove it from the pipeline.

BAdd a GPU to the model training step.

CEnable caching in all the steps of the Kubeflow pipeline.

DComment out the part of the pipeline that you are not currently updating.

Answer : C

Kubeflow Pipelines allows for efficient use of compute resources through parallel task execution and caching, which eliminates redundant executions1. By enabling caching in all the steps of the Kubeflow pipeline, you can avoid re-running the same steps when you execute the pipeline multiple times.This can significantly speed up the pipeline execution and reduce your development time without incurring additional costs

Question 6

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user's recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh dat

a. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

ACreate a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.

BCapture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user's selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production.

CDeploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.

DConfigure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model.

Answer : C

Traffic splitting is a feature of Vertex AI that allows you to distribute the prediction requests among multiple models or model versions within the same endpoint. You can specify the percentage of traffic that each model or model version receives, and change it at any time. Traffic splitting can help you test the new model in production without creating a new endpoint or a separate service. You can deploy the new model to the existing Vertex AI endpoint, and use traffic splitting to send 5% of production traffic to the new model. You can monitor the end-user metrics, such as listening time, to compare the performance of the new model and the previous model. If the end-user metrics improve between models over time, you can gradually increase the percentage of production traffic sent to the new model. This solution can help you test the new model in production while minimizing complexity and cost.Reference:

Traffic splitting | Vertex AI

Deploying models to endpoints | Vertex AI

Question 7

You recently deployed a model lo a Vertex Al endpoint and set up online serving in Vertex Al Feature Store. You have configured a daily batch ingestion job to update your featurestore During the batch ingestion jobs you discover that CPU utilization is high in your featurestores online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?

ASchedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

BEnable autoscaling of the online serving nodes in your featurestore

CEnable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.

DIncrease the worker counts in the importFeaturevalues request of your batch ingestion job.

Answer : B

Vertex AI Feature Store provides two options for online serving: Bigtable and optimized online serving. Both options support autoscaling, which means that the number of online serving nodes can automatically adjust to the traffic demand. By enabling autoscaling, you can improve the online serving performance and reduce the feature retrieval latency during the daily batch ingestion. Autoscaling also helps you optimize the cost and resource utilization of your featurestore.Reference:

Online serving | Vertex AI | Google Cloud

New Vertex AI Feature Store: BigQuery-Powered, GenAI-Ready | Google Cloud Blog