Instant Access Google.Professional-Machine-Learning-Engineer.v2026-07-28.q219 Actual Practice Test Engine for Free (Page 9)

Question 36

You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do?
Choose 2 answers

A.Include a comprehensive set of demographic features.
B.include only the demographic groups that most frequently interact with advertisements.
C.Collect a random sample of production traffic to build the training dataset.
D.Collect a stratified sample of production traffic to build the training dataset.
E.Conduct fairness tests across sensitive categories and demographics on the trained model.

Question 37

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

A.
B.
C.
D.

Question 38

You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

A.1, Maintain the same machine type on the endpoint.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert add a compute node to the endpoint
B.1 Change the machine type on the endpoint to have 32 vCPUs
2. Set up a monitoring job and an alert for CPU usage
3 If you receive an alert, scale the vCPUs further as needed
C.1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert investigate the cause
D.1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.
2 Set up a monitoring job and an alert for GPU usage.
3 If you receive an alert investigate the cause.

Correct Answer: C

Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with 8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage. Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources. You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model. You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. Reference:
[Vertex AI Endpoint documentation]
[Autoscaling documentation]
[Monitoring documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

Question 39

You are developing a custom TensorFlow classification model based on tabular dat a. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

A.1 Write a SQL query to create a separate lookup table to scale the numerical features.
2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.
3. Feed the resulting BigQuery view into Vertex Al Training.
B.1 Use BigQuery to scale the numerical features.
2. Feed the features into Vertex Al Training.
3 Allow TensorFlow to perform the one-hot text encoding.
C.1 Use TFX components with Dataflow to encode the text features and scale the numerical features.
2 Export results to Cloud Storage as TFRecords.
3 Feed the data into Vertex Al Training.
D.1 Write a SQL query to create a separate lookup table to scale the numerical features.
2 Perform the one-hot text encoding in BigQuery.
3. Feed the resulting BigQuery view into Vertex Al Training.

Question 40

You have created a Vertex AI pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket. The second step uses the processed data to train a model. You need to update the model's code to allow you to test different algorithms. You want to reduce pipeline execution time and cost while also minimizing pipeline changes. What should you do?

A.Enable caching for the pipeline job, and disable caching for the model training step.
B.Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.
C.Add a pipeline parameter and an additional pipeline step. Depending on the parameter value, the pipeline step conducts or skips data preprocessing, and starts model training.
D.Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.

Question 36

Question 37

Question 38

Question 39

Question 40

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