PMLE Practice Test 15 — 15 Questions

Question 1

A company wants to implement a CI/CD pipeline for their ML models using Vertex AI. They need to automatically retrain the model when new data arrives, but only if the model performance on a validation set has degraded by more than 5% compared to the current production model. Which three services or components should they incorporate into the automated pipeline? (Choose three.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Vertex AI Evaluation component to compute model performance metrics on the validation set. Option B is correct because the Vertex AI Evaluation component is the mechanism that computes model performance metrics (such as accuracy, AUC, or RMSE) on a validation set, which is exactly what is needed to compare the newly trained model against the current production model and detect a degradation greater than 5%. Option C is correct because Cloud Functions can be configured with a Cloud Storage trigger (via Eventarc) to fire when new data objects arrive in a bucket, thereby automatically kicking off the retraining pipeline without manual intervention. Option D is correct because the Vertex AI Model Registry uses aliases (for example, a 'production' alias) to point to a specific model version, and updating that alias is how a newly validated model gets promoted to production once it passes the performance threshold. Option A is not required by the scenario, since the question focuses on triggering, evaluating, and promoting models rather than on data cleaning, and no data quality issue is stated. Option E is incorrect because Cloud Scheduler runs jobs on a fixed time-based schedule, whereas the requirement is event-driven retraining triggered by the arrival of new data.

Answer

Dataflow pipeline to clean the new data before training

Answer

Cloud Scheduler to run the pipeline on a fixed schedule

Question 2

A company wants to run batch predictions on millions of records stored in BigQuery. They need to preprocess the data (e.g., feature engineering) before feeding it to the model. Which approach is most scalable and cost-effective?

Accepted Answer

Preprocess with Cloud Dataflow, output to Cloud Storage, then submit a Vertex AI batch prediction job.. The most scalable and cost-effective because Cloud Dataflow (Apache Beam) provides serverless, auto-scaling preprocessing that handles large volumes of data efficiently, and Vertex AI batch predictions natively read from Cloud Storage, avoiding the need to manage infrastructure. This decouples preprocessing from prediction, allowing each to scale independently and minimizing costs by using ephemeral, pay-per-use resources.

Answer

Use a large DataProc cluster to preprocess and run batch predictions.

Answer

Preprocess inline in the batch prediction job using a custom container.

Answer

Use a custom Python script on a Compute Engine instance.

Question 3

You are an ML engineer at a logistics company. The company uses a Vertex AI Pipeline with BigQuery ML to train a model that predicts delivery delays based on weather, traffic, and historical order data. The pipeline runs daily and includes steps: (1) data extraction from BigQuery, (2) feature engineering using Dataflow, (3) model training with BigQuery ML (logistic regression), (4) model evaluation, and (5) conditional deployment to a Vertex AI Endpoint if accuracy > 0.85. Recently, the pipeline has been failing at step 5 with the error: "Vertex AI Endpoint creation failed: Quota limit of 1 endpoint per region exceeded." The company has already created one endpoint in the same region for another model. The pipeline is configured to create a new endpoint each time a model is deployed. The engineer needs to fix this with minimal changes to the pipeline code. Which course of action should the engineer take?

Accepted Answer

Modify the deployment step to check if an endpoint already exists and, if so, deploy a new model version to the existing endpoint instead of creating a new one.. It directly addresses the root cause: the pipeline fails because it tries to create a new endpoint each time, exceeding the regional quota of one endpoint. By modifying the deployment step to check for an existing endpoint and deploying a new model version to it, the engineer avoids quota issues without altering the pipeline's core logic or requiring external approvals. This approach leverages Vertex AI's model versioning capability, which allows multiple model versions under a single endpoint, aligning with minimal code changes.

Answer

Submit a quota increase request to Google Cloud for Vertex AI Endpoints in the current region.

Answer

Change the region in the pipeline configuration to a region with available endpoint quota.

Answer

Remove the accuracy threshold and deploy every model automatically to a pre-created endpoint.

Question 4

A machine learning engineer needs to share a trained model with the product team for integration. The model is stored in Cloud Storage, and the product team’s service account needs read access. The engineer wants to follow the principle of least privilege. Which IAM configuration should be used?

Accepted Answer

Grant the product team's service account the roles/storage.objectViewer role at the bucket level.. Granting the product team's service account the roles/storage.objectViewer role at the bucket level provides read-only access to objects in that specific bucket, adhering to the principle of least privilege. This role allows the service account to list and read objects without granting broader permissions, such as modifying or deleting them, and scoping it to the bucket prevents unnecessary access to other buckets in the project.

Answer

Generate a signed URL with read access and share it with the product team.

Answer

Grant the product team's service account the roles/storage.objectAdmin role at the bucket level.

Answer

Grant the product team's service account the roles/storage.objectViewer role at the project level.

Question 5

Your ML pipeline uses Vertex AI Feature Store to serve features for online predictions. You need to monitor the freshness of features in the online store. Which approach is most effective?

Accepted Answer

Create a custom metric in Cloud Monitoring that tracks the time since last feature update, and set an alert threshold.. Cloud Monitoring custom metrics allow you to track the timestamp of the last feature update in Vertex AI Feature Store and set an alert threshold for staleness. This directly measures feature freshness, which is critical for online predictions where stale features can degrade model accuracy. Other options either measure unrelated metrics (entity count), are too slow (nightly batch), or focus on auditing rather than real-time monitoring.

Answer

Set up a Cloud Monitoring alert for feature store entity count.

Answer

Schedule a nightly BigQuery batch job to compare feature values.

Answer

Enable detailed audit logs in Feature Store and export to BigQuery.

Question 6

An e-commerce company uses a Vertex AI endpoint for product recommendations. Recently, the click-through rate (CTR) dropped significantly. Model monitoring shows no significant data drift or skew. Logs show increased latency but no errors. Which technique should the engineer use to diagnose the issue?

Accepted Answer

Analyze the prediction output distribution using Vertex AI Model Monitoring for prediction drift and compare to a baseline.. The drop in CTR despite no data drift or skew suggests that the model's predictions have shifted in distribution (prediction drift), even if the input features remain stable. Vertex AI Model Monitoring can compare the current prediction output distribution against a baseline to detect such drift, which directly explains the CTR decline. The increased latency is a symptom, not the root cause, and fixing latency alone would not restore CTR.

Answer

Increase the endpoint's request timeout value to accommodate the higher latency.

Answer

Enable autoscaling on the endpoint to reduce latency by adding more nodes.

Answer

Retrain the model with the most recent user interaction data.

Question 7

An ML engineer is designing a pipeline that should run only when new training data arrives in a Cloud Storage bucket. Which event-driven approach should they use to trigger the Vertex AI Pipeline?

Accepted Answer

Use Cloud Storage Pub/Sub notifications to send events to a Cloud Function that triggers the pipeline.. The best approach is to use Cloud Storage notifications via Pub/Sub, then a Cloud Function that receives the event and calls the Vertex AI API to create a pipeline job. This is a common event-driven pattern. Cloud Scheduler is for scheduled triggers, not event-driven. Cloud Tasks and Cloud Run are not typically used for this purpose.

Answer

Use Cloud Tasks to queue a pipeline run whenever a new file is uploaded.

Answer

Configure the pipeline to run on a schedule and check for new data inside the pipeline.

Answer

Set up a Cloud Scheduler job that runs every minute to check for new files.

Question 8

A company has deployed a machine learning model that uses a large input tensor. They notice that the prediction latency varies significantly between requests of the same size. Cloud Monitoring shows that the serving endpoint's CPU utilization is consistently below 50%, but memory utilization fluctuates between 70% and 95%. What is the most likely cause?

Accepted Answer

The model is performing garbage collection cycles. The described symptoms—low CPU utilization (below 50%) and high, fluctuating memory utilization (70%–95%) with variable latency—are classic indicators of garbage collection (GC) pauses in a managed runtime like Python or Java. When the model processes large input tensors, it allocates significant memory; as memory pressure builds, the garbage collector runs more frequently, causing stop-the-world pauses that increase latency unpredictably, even though CPU is not fully utilized.

Answer

The model is using excessive memory due to a memory leak

Answer

The prediction latency is being affected by CPU throttling

Answer

The model is hitting a cold start due to autoscaling

Question 9

Your company deploys batch prediction jobs using Vertex AI Batch Prediction. You need to monitor the jobs for failures and performance. What is the recommended approach?

Accepted Answer

Use Cloud Monitoring to create custom dashboards and alerts based on Vertex AI batch prediction metrics.. Cloud Monitoring (formerly Stackdriver) is the native Google Cloud service for collecting, visualizing, and alerting on metrics from Vertex AI, including batch prediction job success rates, latency, and resource utilization. It provides pre-built dashboards and the ability to create custom alerts, making it the recommended approach for monitoring failures and performance in a centralized, scalable way.

Answer

Use Cloud Logging to export batch prediction logs and create log-based metrics.

Answer

Set up email alerts in the Vertex AI console for failed jobs.

Answer

Enable the Recommender to get optimization suggestions for batch jobs.

Question 10

An ML engineer wants to containerize a custom training script and use it as a component in a Vertex AI Pipeline. The component should accept a dataset URI and a learning rate parameter, and output a trained model artifact. Which approach should the engineer use to define the component?

Accepted Answer

Use ContainerComponent from kfp.v2.components to define the container, its inputs, and outputs.. ContainerComponent from kfp.v2.components allows you to define a custom container component by specifying the container image, command, inputs, and outputs directly. This is the appropriate approach when you have a custom training script that you want to containerize and use as a component in a Vertex AI Pipeline, as it gives you full control over the container configuration and artifact handling.

Answer

Use a pre-built Google Cloud Pipeline Component for Vertex AI Training with custom container configuration.

Answer

Define a Python function component with @dsl.component and include the container code inline.

Answer

Use the importer component to import the script and then run it as a task.

Question 11

A company is using Vertex AI Pipelines for ML workflows. They want to implement best practices for idempotent components and data passing. Which THREE practices should they adopt?

Accepted Answer

Pass large datasets between components using GCS URIs instead of in-memory values.. Option A is correct because Vertex AI Pipelines components exchange data through artifacts, and passing large datasets as GCS URIs (e.g., gs://bucket/path) avoids serializing huge in-memory objects into the pipeline's metadata/execution store, keeping runs efficient and reproducible. Option B is correct because hard-coded paths break portability and reproducibility across environments; using pipeline parameters (or input artifacts) to supply URIs lets the same pipeline definition run against dev, test, or prod buckets without code changes. Option E is correct because idempotent components—where identical inputs always yield identical outputs and re-execution has no additional side effects—are a core Vertex AI Pipelines best practice, enabling safe retries and caching. Option C is not appropriate because passing in-memory objects between components requires serialization, can exceed metadata limits, and undermines the artifact-based data-passing model. Option D is not appropriate because global variables introduce hidden state, are not tracked as pipeline parameters or artifacts, and break reproducibility and caching across pipeline runs.

Answer

Read data into memory in the first component and pass the in-memory object to subsequent components.

Answer

Use global variables in the pipeline code to store intermediate results.

Question 12

A team deploys a model using Vertex AI and wants to monitor for concept drift. What should they track?

Accepted Answer

Changes in the relationship between inputs and outputs. Concept drift refers to a change in the underlying relationship between the input features and the target variable over time, which degrades model performance. In Vertex AI, monitoring this requires tracking the statistical relationship between inputs and outputs (e.g., via prediction residuals or model performance metrics), not just the input distribution alone. Option D correctly identifies this need, as concept drift is fundamentally about the input-output mapping shifting, even if the input distribution remains stable.

Answer

Number of prediction requests

Answer

Model prediction latency

Answer

Changes in input data distribution

Question 13

A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store is purpose-built to store, share, and serve machine learning features with low-latency online serving and consistent offline serving for training. It lets a data science team centralize engineered features so multiple projects reuse them, while providing an online serving endpoint that returns feature values in milliseconds for real-time predictions.

Answer

Vertex AI Model Registry

Answer

Cloud Storage

Answer

BigQuery

Question 14

A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is served using TensorFlow Serving on a Google Kubernetes Engine (GKE) cluster with auto-scaling enabled. The cluster uses n1-standard-4 machine types. The team has set up Cloud Monitoring dashboards and alerts. Last week, during a major holiday promotion, the team noticed that the model's inference latency P99 increased from 150 ms to 450 ms over a 30-minute period, while the request throughput increased from 500 to 1,200 requests per second. CPU utilization across the cluster rose to 95%, but memory utilization remained at 60%. The model version and the serving infrastructure configuration have not changed since the last deployment. Which action should the team take to mitigate the latency issue?

Accepted Answer

Add more nodes to the GKE cluster to increase the total CPU resources available for serving.. The latency spike is caused by CPU saturation (95% utilization) under increased load (500 to 1,200 RPS). Adding more nodes to the GKE cluster directly increases the total CPU resources available, allowing the existing TensorFlow Serving pods to handle the higher throughput without contention. This is the most immediate and infrastructure-appropriate fix because the model version and serving configuration have not changed, ruling out model-level or code-level optimizations.

Answer

Implement a feature engineering pipeline that compresses the input features to reduce data size and inference time.

Answer

Deploy a newer version of the model that uses a more efficient architecture to reduce computational complexity.

Answer

Increase the number of TensorFlow Serving instances by reducing the CPU request per pod in GKE to allow more pods per node.

Question 15

A team is using Delta Lake on Dataproc for their data lake with ACID transactions. They want to version data for ML experiments and roll back to a previous version if needed. Which Delta Lake feature should they use?

Accepted Answer

Delta Lake time travel. Delta Lake time travel allows querying previous versions of a Delta table using either a version number or a timestamp, enabling reproducibility for ML experiments and rollback to a prior state. This is the native Delta Lake feature designed for versioning and auditing data changes. It directly satisfies the requirement to version data and roll back.

Answer

Delta Lake streaming

Answer

Delta Lake schema enforcement

Answer

Delta Lake optimization (Z-order)