PMLE Practice Test 29 — 15 Questions

Question 1

Which THREE should be considered when setting up an automated retraining pipeline using Vertex AI Pipelines and Cloud Composer? (Choose THREE.)

Accepted Answer

Setting performance thresholds for new models to decide deployment. In an automated retraining pipeline, you must set performance thresholds (e.g., accuracy, precision, recall) for new models to decide whether to deploy them. Vertex AI Pipelines can evaluate model metrics against these thresholds and conditionally deploy only if the new model meets or exceeds the current production model's performance, preventing regressions.

Answer

Including hyperparameter tuning in every retraining run

Answer

Frequency of code commits to the repository

Question 2

A data science team uses Vertex AI Pipelines to build a training pipeline. They notice that when the pipeline fails due to a transient error in a component, the entire pipeline restarts from the beginning, taking a long time. What is the best practice to handle transient errors efficiently?

Accepted Answer

Configure Vertex AI Pipelines to automatically restart from the last successful state by enabling checkpointing.. Vertex AI Pipelines supports checkpointing, which allows a pipeline to resume from the last successful state after a transient failure, avoiding a full restart. This is the most efficient approach for handling transient errors in a managed pipeline service, as it minimizes wasted compute time and resources.

Answer

Use Vertex AI Experiment to track runs and manually restart failed components.

Answer

Wrap the component code in a try-except block and retry indefinitely.

Answer

Set the component's retry count to 3 in the pipeline definition.

Question 3

Which TWO of the following are best practices for versioning ML models and datasets?

Accepted Answer

Use Vertex AI Model Registry for model versioning and lineage tracking.. Vertex AI Model Registry is a managed service that automatically tracks model versions, artifacts, and lineage metadata (e.g., training runs, evaluation metrics, and source datasets). It provides a centralized hub for model governance, enabling reproducibility and auditability without manual versioning overhead. This makes it a best practice for versioning ML models in a production MLOps workflow.

Answer

Store datasets and models in the same Cloud Storage bucket with version prefixes.

Answer

Use Git LFS for dataset versioning.

Answer

Use Cloud Data Catalog to tag dataset versions.

Question 4

A company uses Vertex AI Pipelines for model training and deployment. The pipeline includes a model evaluation step that produces metrics. If the metrics are below a threshold, the pipeline should fail and not deploy. Which component should they use?

Accepted Answer

Use a conditional operator in the pipeline to skip or fail based on metrics.. Vertex AI Pipelines supports conditional execution via the `Condition` component or `if/else` operators within the pipeline DAG. This allows you to evaluate model metrics (e.g., accuracy, AUC) and, if they fall below a defined threshold, either skip the deployment step or explicitly fail the pipeline using `PipelineTask.fail()` or a conditional branch that raises an error. This is the native, declarative way to control pipeline flow based on evaluation results without relying on external services or manual intervention.

Answer

A Python component that uses the SDK to raise an exception if metrics are low.

Answer

A Vertex AI Model Evaluation component configured with a threshold.

Answer

Use Cloud Monitoring to trigger an alert and manually stop deployment.

Answer

A custom container that returns a non-zero exit code on failure.

Question 5

A financial services company wants to detect fraudulent transactions in real-time. They have a trained XGBoost model that runs on a single Compute Engine instance. The current solution processes about 100 transactions per second, but they need to scale to 10,000 transactions per second. Which approach should they take?

Accepted Answer

Deploy the model to Vertex AI Prediction with autoscaling enabled. Vertex AI Prediction with autoscaling is the correct choice because it is purpose-built for serving ML models at scale, automatically adjusting the number of compute nodes based on incoming request traffic. This allows the company to seamlessly handle the increase from 100 to 10,000 transactions per second without manual intervention, while XGBoost is natively supported as a framework.

Answer

Increase the VM to a machine type with more vCPUs and memory

Answer

Use Dataflow to process transactions in micro-batches every second

Answer

Rewrite the model as a Cloud Function triggered by Pub/Sub messages

Question 6

Which THREE are best practices for implementing CI/CD for ML pipelines on Google Cloud? (Choose THREE.)

Accepted Answer

Maintain separate environments for dev, staging, and production. Maintaining separate environments for dev, staging, and production is a core CI/CD best practice because it isolates changes, prevents accidental breakage in production, and allows thorough validation at each stage. On Google Cloud, this aligns with using distinct Vertex AI Pipelines instances or separate projects to enforce environment-specific configurations and access controls.

Answer

Design pipelines with low-code components to reduce development time

Answer

Write unit tests for every training job

Question 7

A team is responsible for monitoring the health of a Vertex AI pipeline that runs daily. Which THREE resources should they use to gain visibility into pipeline performance and failures? (Choose 3.)

Accepted Answer

Vertex AI Experiments for comparing pipeline runs. Vertex AI Experiments (Option C) is correct because it provides a systematic way to log, compare, and analyze pipeline runs, including metrics, parameters, and artifacts. This allows the team to track performance trends across daily runs, identify regressions, and correlate failures with specific run configurations, which is essential for monitoring pipeline health over time.

Answer

Cloud Trace for analyzing distributed execution

Answer

Cloud Composer for tracking DAGs

Question 8

An ML engineer runs this command to upload a model. The model artifact in Cloud Storage is a directory containing model.pkl and a custom preprocessing script. What will happen when he later deploys this model to an endpoint and sends a prediction request?

Accepted Answer

The prediction will fail because the custom preprocessing script is not a standard scikit-learn serialized object.. The pre-built container for scikit-learn expects a single serialized model file (e.g., model.pkl) as the artifact. A directory containing a custom preprocessing script is not a standard scikit-learn serialized object, so the container cannot load or execute it, causing the prediction to fail.

Answer

The prediction will succeed because the pre-built container automatically detects and uses the custom preprocessing script.

Answer

The prediction will succeed only if he also specifies a custom prediction routine.

Answer

The prediction will fail because the artifact URI must point to a single file not a directory.

Question 9

A company deploys a model on Vertex AI Prediction for real-time inference. Users report intermittent high latency during peak hours. The model is deployed on a single machine type with `min_replica_count=1` and `max_replica_count=5`. Autoscaling is enabled based on CPU utilization. What is the most likely cause of the latency spikes?

Accepted Answer

Autoscaling based on CPU utilization does not react quickly to inference request spikes.. CPU utilization may not be a good proxy for inference load; the system may not scale up fast enough under sudden traffic bursts. Option A is wrong because Vertex AI automatically manages container health. Option C is wrong because Vertex AI endpoints automatically distribute traffic. Option D is wrong because the container image is built correctly.

Answer

The model server is crashing under load due to memory issues.

Answer

The load balancer is misconfigured and routes traffic unevenly.

Answer

The container image is not optimized for the model.

Question 10

You run the above command to deploy a new model version to an existing endpoint. After deployment, you observe that the endpoint's previous model version is still receiving 100% of traffic. What is the most likely reason for this?

Accepted Answer

The --traffic-split flag is specified incorrectly; it should use model IDs, not '0-100'.. The traffic-split flag syntax is incorrect. The correct syntax for Vertex AI is --traffic-split== for each model. Without correct model IDs, the flag is ignored, and no traffic split is applied, so the existing version continues to receive all traffic.

Answer

The new model is still in the 'creating' state and hasn't been activated.

Answer

The model ID provided does not exist in the endpoint.

Answer

The min-replica-count is too high, preventing traffic splitting.

Question 11

A company deploys a training pipeline on Vertex AI using custom containers. The pipeline includes a hyperparameter tuning job that uses Bayesian optimization. After several runs, they observe that the tuning job is not converging and the search space is large. They want to reduce the number of trials while still finding good hyperparameters. Which strategy should they use?

Accepted Answer

Reduce the search space by applying feature selection and using prior knowledge.. Reducing the search space using prior knowledge directly decreases the number of trials needed. Option A is wrong because increasing parallel trials does not reduce the total number of trials. Option B is wrong because grid search generally requires more trials than Bayesian optimization. Option C is wrong because early stopping reduces time per trial but does not reduce the number of trials.

Answer

Increase the number of parallel trials to explore more points simultaneously.

Answer

Use Grid search instead of Bayesian optimization to systematically cover the search space.

Answer

Implement early stopping by using the 'early_stopping' flag in the hyperparameter tuning job.

Question 12

Refer to the exhibit. A team uses this Cloud Build configuration to deploy a model to a Vertex AI endpoint. The build succeeds up to the 'upload' step, but the 'deploy-model' step fails with an error that the model 'my-model' does not exist. What is the most likely cause?

Accepted Answer

The model was not uploaded because the artifact URI is a directory, not a valid SavedModel. The 'deploy-model' step fails because the model was not successfully uploaded. Cloud Build's 'upload' step expects a valid SavedModel artifact (a directory containing a saved_model.pb file and variables subdirectory). If the artifact URI points to a directory that is not a valid SavedModel, the upload may appear to succeed but does not register a usable model resource, causing the subsequent deploy step to fail with 'model does not exist'.

Answer

The deploy step uses the display name instead of the model resource ID

Answer

The Vertex AI API was not enabled for the project

Answer

The region in the deploy step does not match the model's region

Question 13

Refer to the exhibit. What is this Cloud Build step doing?

Accepted Answer

Uploading a model to Vertex AI Model Registry. The Cloud Build step shown uses the `gcloud ai models upload` command, which specifically uploads a model artifact to the Vertex AI Model Registry. This action registers the model metadata and location in Vertex AI, making it available for versioning and later deployment, but does not create an endpoint or perform training.

Answer

Deploying a model to a Vertex AI endpoint

Answer

Creating a custom container for prediction

Answer

Training a model in Vertex AI

Question 14

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Accepted Answer

Initialize the Vertex AI SDK, then create an experiment, then log metrics and parameters, then record artifacts, then review lineage.. Start with SDK setup, then create an experiment, log metrics, record artifacts, and review lineage.

Answer

Create an experiment, then initialize the SDK, then log metrics, then review lineage, then record artifacts.

Answer

Initialize the SDK, then log metrics and parameters, then create an experiment, then record artifacts, then review lineage.

Answer

Initialize the SDK, then create an experiment, then record artifacts, then log metrics, then review lineage.

Question 15

A retail company wants to predict customer churn using their transaction history and customer demographics. They have limited ML expertise and want to use a managed service on Google Cloud. Which service should they use?

Accepted Answer

Vertex AI AutoML (Tables). Vertex AI AutoML (Tables) is the correct choice because it is a managed service specifically designed for tabular data, requiring no ML expertise. It automates model training, hyperparameter tuning, and deployment for classification tasks like churn prediction, directly handling transaction history and demographic features.

Answer

AI Platform Notebooks

Answer

Cloud TPU

Answer

BigQuery ML