PMLE Practice Test 14 — 15 Questions

Question 1

A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?

Accepted Answer

Add more labeled images to the dataset. Adding more labeled images directly addresses the most common cause of low accuracy in AutoML vision models: insufficient or unrepresentative training data. Vertex AI AutoML relies on transfer learning from pre-trained models, and its performance is heavily dependent on the quality and quantity of labeled examples. Before adjusting hyperparameters or infrastructure, the first step should always be to improve the dataset, as AutoML is designed to handle model architecture and training budget automatically.

Answer

Switch to a custom model

Answer

Increase the training budget

Answer

Reduce image size to speed up training

Question 2

Which TWO of the following are best practices for managing data in a collaborative machine learning environment on Google Cloud?

Accepted Answer

Use Cloud Data Catalog to discover and annotate datasets.. Cloud Data Catalog provides a managed metadata management service that allows teams to discover, annotate, and manage datasets across Google Cloud. It enables data scientists to search for datasets by tags, descriptions, and schema, which is essential for collaboration and data governance in a multi-user ML environment.

Answer

Always replicate data across multiple regions to ensure low latency.

Answer

Implement fine-grained access control using IAM conditions.

Answer

Store all raw data in a single Cloud Storage bucket for easy access.

Question 3

You are using Vertex AI Vector Search with an approximate nearest neighbor index. You need to update the index with new data every hour. The updates must be available for queries immediately. Which update method should you use?

Accepted Answer

Streaming updates using the streaming API.. Vertex AI Vector Search supports streaming updates via its streaming API, which allows you to insert, update, or delete vectors in real time. This ensures that new data is immediately available for approximate nearest neighbor (ANN) queries without requiring index recreation or redeployment, meeting the requirement for hourly updates with instant query availability.

Answer

Recreate the index every hour using a scheduled job.

Answer

Batch update by creating a new index and deploying it.

Answer

Use a brute-force index that supports real-time updates.

Question 4

A company has a Vertex AI pipeline that trains a model on streaming data from Pub/Sub. The pipeline is triggered by a Cloud Function when new data arrives. Recently, jobs have been failing with 'ResourceExhausted: Quota limit exceeded for regional CPUs in us-central1.' The team needs to ensure successful job execution while minimizing changes. Which approach should they take?

Accepted Answer

Configure the pipeline's training job to use preemptible VMs (which count toward a separate, usually higher quota).. Preemptible VMs count toward a separate, often higher quota for 'Preemptible CPUs' rather than the standard regional CPU quota. By configuring the training job to use preemptible VMs, the team can bypass the exhausted quota without requesting a limit increase or changing the pipeline architecture. This minimizes changes while leveraging the fact that Vertex AI training jobs can be configured to use preemptible VMs via the `worker_pool_specs` with `accelerator_type` and `machine_type` settings.

Answer

Request a quota increase from Google Cloud Support.

Answer

Change the pipeline to run in a different region with available quota.

Answer

Reduce the number of parallel pipeline runs by using a Cloud Tasks queue with rate limiting.

Question 5

Match each ML pipeline component to its description.

Accepted Answer

Data ingestion: Collecting and importing raw data from various sources.. The correct matches are: Data ingestion → Collecting and importing raw data from various sources. Data validation → Checking data quality, integrity, and schema compliance. Feature engineering → Transforming raw data into features that improve model performance. The remaining options are incorrect: Model training is not about assessing model performance (that's evaluation); Model evaluation is not about deploying a model (that's deployment); Model deployment is not about transforming raw data into features (that's feature engineering).

Answer

Model training: The process of assessing model performance using metrics.

Answer

Model evaluation: The process of deploying a model into production.

Answer

Model deployment: The process of transforming raw data into features.

Question 6

Refer to the exhibit. A data scientist deploys a new model version (model_v2) to an existing endpoint with 20% traffic. After a few days, they notice that model_v2's error rate is higher than model_v1's. They want to route all traffic back to model_v1 immediately. Which command achieves this with minimal disruption?

Accepted Answer

gcloud ai endpoints update my-endpoint --region=us-central1 --traffic-split=model_v1=1,model_v2=0. The `gcloud ai endpoints update` command with the `--traffic-split` flag allows you to set the traffic distribution among deployed models. By specifying `model_v1=1,model_v2=0`, all traffic is immediately routed to model_v1 without removing or redeploying any models, minimizing disruption. Option A is incorrect because `--remove-deployed-model` removes the model entirely, which may cause downtime if not re-deployed, and the command syntax is not valid for traffic rerouting. Option B has a misspelled subcommand (`undepoly` instead of `undeploy`) and would undeploy the model, causing temporary unavailability. Option D uses an invalid command (`update-traffic`) and syntax; the correct approach is to use `--traffic-split` on the update command.

Answer

gcloud ai endpoints update my-endpoint --region=us-central1 --remove-deployed-model=model_v2

Answer

gcloud ai endpoints undepoly-model my-endpoint --region=us-central1 --model=model_v2

Answer

gcloud ai endpoints update-traffic my-endpoint --region=us-central1 --model=model_v1 --traffic-percentage=100

Question 7

You have deployed a regression model that predicts house prices. Over the past month, the model's predictions have been consistently too high. You suspect data drift in the input features. Which monitoring metric should you prioritize to confirm this?

Accepted Answer

Monitor feature distribution drift using a divergence metric like Jensen-Shannon divergence. The question describes a scenario where predictions are consistently too high, which is a symptom of data drift—a change in the distribution of input features. Monitoring feature distribution drift using a divergence metric like Jensen-Shannon divergence directly measures whether the input data has shifted from the training distribution, which would cause the model to make biased predictions. This is the most direct way to confirm data drift in the input features.

Answer

Monitor prediction drift (prediction distribution)

Answer

Monitor feature attribution drift using SHAP values

Answer

Monitor residual distribution drift

Question 8

A financial services company uses Vertex AI to deploy multiple models for fraud detection. The ML team has set up a CI/CD pipeline using Cloud Build and Cloud Deploy. The pipeline builds a custom container with the trained model, pushes it to Artifact Registry, and deploys it to a Vertex AI Endpoint. Recently, a new regulation requires that all model deployments be audited and approved by the compliance team before going live. The compliance team wants to review the model's evaluation metrics and approve the deployment via a ticketing system. Currently, the CI/CD pipeline automatically deploys after the container is built. The team needs to implement a gating process without slowing down the development cycle. What should they do?

Accepted Answer

Modify the CI/CD pipeline to use Cloud Deploy's approval gate feature, requiring a manual approval from the compliance team before the deployment step.. Cloud Deploy provides a native approval gate feature that can be inserted into a delivery pipeline to require manual sign-off before a deployment proceeds. This allows the compliance team to review model evaluation metrics and approve via a ticketing system without modifying the CI/CD pipeline's build process, thus maintaining development velocity. The approval gate pauses the deployment at a specific stage, waiting for an external approval signal, which integrates seamlessly with Cloud Deploy's rollout management.

Answer

Use Cloud Composer to orchestrate the deployment and add a sensor that waits for approval from the ticketing system via a custom operator.

Answer

Use Cloud Build's built-in approval gate feature to require compliance team sign-off before deployment.

Answer

Store the model artifacts in Cloud Storage and have the compliance team deploy manually using the gcloud command.

Question 9

A machine learning engineer wants to use Vertex AI Vizier to tune three hyperparameters: learning rate (log scale), number of layers (integer), and optimizer (categorical). They have 50 parallel trials available. Which parameter specification types should they define?

Accepted Answer

learning_rate: DOUBLE (unit_log_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL. In Vertex AI Vizier, continuous parameters use DOUBLE type (can be scaled log), integer parameters use INTEGER, and categorical parameters use CATEGORICAL. The scale type for learning rate should be UNIT_LOG_SCALE for log scale.

Answer

learning_rate: CATEGORICAL, layers: INTEGER, optimizer: CATEGORICAL

Answer

learning_rate: DOUBLE (unit_log_scale), layers: DOUBLE (unit_linear_scale), optimizer: DISCRETE

Answer

learning_rate: DOUBLE (unit_linear_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL

Question 10

Match each Google Cloud AI/ML service to its primary purpose.

Accepted Answer

Vertex AI: End-to-end ML platform for building, training, and deploying models.. In this matching question, the correct pairings are: Option A (Vertex AI: End-to-end ML platform) is correct because Vertex AI unifies the ML workflow. Option C (Cloud AutoML: Train custom ML models with minimal coding) is correct because AutoML provides no-code training. Option E (Vision API: Analyze images for objects, faces, text) is correct because Vision API specializes in image analysis. Option B is wrong because Vertex AI is not primarily for graphical no-code training—that is AutoML's strength. Option D is wrong because Cloud AutoML is not an end-to-end platform—that's Vertex AI. Option F is wrong because Vision API does not analyze text; that is the Natural Language API's purpose.

Answer

Vertex AI: Train custom ML models using a graphical interface with minimal coding.

Answer

Cloud AutoML: End-to-end ML platform for building, training, and deploying models.

Answer

Vision API: Analyze text for sentiment, entity recognition, and syntax.

Question 11

A company uses Cloud Scheduler to trigger Cloud Functions that submit Vertex AI training jobs. They want to ensure fault tolerance and minimize manual intervention. Which TWO practices should they implement?

Accepted Answer

Use Cloud Tasks with retries to handle failed triggers.. Cloud Tasks provides built-in retry logic with exponential backoff, which can reliably handle transient failures when triggering Cloud Functions from Cloud Scheduler. By configuring a Cloud Tasks queue with retry parameters, the system automatically retries failed triggers without manual intervention, ensuring fault tolerance for Vertex AI training job submissions.

Answer

Store training hyperparameters in Cloud Firestore for reproducibility.

Answer

Use Cloud Run jobs as an alternative execution environment.

Answer

Implement a fallback that runs the job on Compute Engine if Vertex AI fails.

Question 12

A data scientist has deployed a model on Vertex AI Endpoints and wants to monitor the model's predictions for any drift over time. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is specifically designed to monitor deployed models for feature drift, feature skew, and prediction drift. It uses statistical methods to compare serving distributions over time or against training data.

Answer

Vertex AI Feature Store

Answer

Vertex AI Predictions

Answer

Vertex AI Explainable AI

Question 13

An ML engineer has deployed a model on Vertex AI Endpoints and wants to detect when the serving data distribution differs from the training data distribution. Which monitoring feature should they enable?

Accepted Answer

Feature skew monitoring. Feature skew monitoring compares the training data distribution (stored in a baseline) with the serving data distribution to detect skew. Feature drift tracks changes over time in serving data only.

Answer

Prediction drift monitoring

Answer

Feature drift monitoring

Answer

Model quality monitoring

Question 14

A company wants to monitor the cost of their Vertex AI prediction endpoint. They are charged per hour per replica and per request for GPU instances. Which approach should they use to track these costs?

Accepted Answer

Set up Cloud Billing budget alerts and export billing data to BigQuery for analysis. Vertex AI prediction costs are tracked via Cloud Billing. Budget alerts can be set up to notify when spending exceeds a threshold. Cost breakdown can be viewed in the Billing reports.

Answer

Use Vertex AI Model Monitoring to track cost metrics

Answer

Enable Cloud Monitoring dashboards for cost metrics

Answer

Use Vertex AI Pipelines to track cost per job

Question 15

An organisation wants to monitor fairness of their loan approval model across demographic subgroups. They have predictions stored in BigQuery along with ground truth. Which GCP service can evaluate model performance for each subgroup and identify disparities?

Accepted Answer

Vertex AI Model Evaluation. Vertex AI Model Evaluation supports sliced evaluation, where metrics are computed for each subgroup defined by feature values (e.g., gender, race) in BigQuery. This helps identify performance disparities.

Answer

Cloud Data Loss Prevention (DLP)

Answer

Vertex AI Explainable AI

Answer

Vertex AI Model Monitoring