PMLE Practice Test 17 — 15 Questions

Question 1

An ML engineer is designing a CI/CD pipeline for ML models using Cloud Build and Cloud Deploy. They want to automatically test model performance on a validation set before promoting to production. Which step should be included in the CI/CD pipeline?

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

Accepted Answer

Run a Vertex AI Pipeline for model evaluation and register the model only if metrics exceed thresholds. It directly integrates model evaluation into the CI/CD pipeline using Vertex AI Pipelines, which allows automated validation of model performance against predefined thresholds before promotion. This ensures that only models meeting quality criteria are deployed, aligning with MLOps best practices for gated promotions.

Answer

Run unit tests on the training code

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Use Cloud Composer to schedule evaluation

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Deploy to production immediately after training

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Train the model in the CI/CD pipeline

Question 2

Which TWO are benefits of using Vertex AI Pipelines for ML workflow orchestration over deploying custom Airflow DAGs in Cloud Composer? (Choose TWO.)

Accepted Answer

Automatic artifact lineage tracking. Option C is correct because Vertex AI Pipelines automatically tracks artifact lineage through ML Metadata, recording inputs, outputs, and parameters of each pipeline step without requiring custom instrumentation. Option D is correct because Vertex AI Pipelines natively integrates with Vertex AI services such as Vertex AI Training, Vertex AI Endpoints, and the Model Registry, enabling seamless handoff between pipeline steps and managed ML resources. In contrast, option A is not the distinguishing benefit here since Cloud Composer is also a managed service that abstracts much of the underlying infrastructure. Option B is incorrect because Cloud Composer (Airflow) also provides robust built-in scheduling capabilities. Option E is incorrect because both Airflow DAGs and Vertex AI Pipelines support arbitrary Python code in their steps, so this is not a unique advantage.

Answer

Managed infrastructure without manual configuration

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Built-in scheduling capabilities

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Support for arbitrary Python code in steps

Question 3

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. Option A is correct because an automated retraining pipeline needs defined performance thresholds (e.g., accuracy, AUC, or RMSE gates) so that a newly trained model is only promoted to deployment when it demonstrably beats or meets the required metric, preventing regression in production. Option C is correct because Vertex AI Pipelines runs training, tuning, and evaluation steps on billable compute, so resource allocation choices such as machine type, accelerator (GPU/TPU) usage, and pipeline caching directly control cost and must be planned for a sustainable retraining cadence. Option E is correct because the trigger for retraining is typically data drift or concept drift detected via Vertex AI Model Monitoring, which emits alerts that Cloud Composer DAGs can consume to launch the pipeline, making drift monitoring a core design consideration. Option B is not required: hyperparameter tuning need not run on every retraining cycle, since it is expensive and often only needed when the model or data distribution changes materially. Option D is not relevant: the frequency of code commits reflects developer workflow, not the operational triggers or resource decisions of an automated retraining pipeline.

Answer

Including hyperparameter tuning in every retraining run

Answer

Frequency of code commits to the repository

Question 4

An MLOps engineer is setting up monitoring for a deployed model on Vertex AI Endpoints. Which TWO actions are required to enable Vertex AI Model Monitoring for feature skew and drift? (Choose two.)

Accepted Answer

Enable request/response logging on the Vertex AI Endpoint. To enable model monitoring, you must enable request/response logging on the endpoint (to capture serving data) and create a monitoring job with the desired configuration.

Answer

Export ground truth labels to Cloud Storage

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Enable Vertex AI Pipelines to run scheduled monitoring

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Deploy the model with an explanation spec

Question 5

A data engineer wants to compute feature aggregates over a large dataset stored in BigQuery and write the results to Vertex AI Feature Store. The pipeline must handle both batch and streaming data. Which Google Cloud service should they use?

Accepted Answer

Cloud Dataflow with Apache Beam. Cloud Dataflow with Apache Beam is a unified stream and batch data processing service. It can read from BigQuery, compute aggregates, and write to Vertex AI Feature Store, handling both batch and streaming data with the same pipeline code. This makes it the ideal choice for the requirement.

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BigQuery scheduled queries

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Cloud Functions triggered by Pub/Sub

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Cloud Dataproc with Spark

Question 6

You are using Vertex AI batch prediction and your model requires preprocessing that involves joining two BigQuery tables. The preprocessing logic is complex and must be done before inference. How should you design the pipeline?

Accepted Answer

Use Dataflow to read from both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source.. Dataflow (Apache Beam) is designed for complex, stateful data processing like joining two BigQuery tables and performing custom preprocessing. It can read from BigQuery, execute the join logic, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction then reads the preprocessed data from GCS, which is the recommended pattern for non-trivial transformations before inference, as it decouples preprocessing from prediction and avoids resource contention.

Answer

Write a Cloud Composer workflow that runs the preprocessing and then triggers the batch prediction job.

Answer

Use Vertex AI batch prediction with a custom container that includes logic to read and join tables on the fly.

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Use BigQuery to create a materialized view that joins the tables and directly use that as the batch prediction source.

Question 7

An MLOps team is implementing a CI/CD pipeline for a TensorFlow model on Vertex AI. The model training job takes 2 hours and produces a SavedModel. The team wants to automatically trigger a new pipeline run whenever a change is pushed to the 'main' branch of their source repository. The pipeline should include training, evaluation, and if metrics exceed a threshold, deploy the model to a Vertex AI endpoint. Which trigger configuration should they use?

Accepted Answer

Configure a Cloud Build trigger that watches the 'main' branch of Cloud Source Repositories; in the build config, use steps to run the pipeline via the Vertex AI API.. Cloud Build triggers can be configured to watch a specific branch (e.g., 'main') in Cloud Source Repositories and automatically execute a build configuration. Within that build config, you can use the `gcloud` or `curl` steps to invoke the Vertex AI Pipeline API, which starts the training, evaluation, and conditional deployment workflow. This directly matches the requirement for a branch-based push trigger that orchestrates the full ML pipeline.

Answer

Use Eventarc to listen for Cloud Source Repository push events and invoke a Cloud Run service that starts the pipeline.

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Use an Artifact Registry trigger to detect new model images and then start the pipeline.

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Set up a Cloud Scheduler job that runs every 2 hours and triggers a Vertex AI Pipeline run.

Question 8

You are deploying a large deep learning model on Vertex AI endpoints. The model requires GPU acceleration and you want to minimize cold-start latency. Which TWO actions should you take? (Choose 2 correct answers)

Accepted Answer

Use a custom container that loads the model during startup.. Option B is correct because using a custom container that loads the model during startup lets you control and optimize the initialization process (for example, preloading weights and warming up the GPU) so the model is ready as soon as the container starts, which directly reduces cold-start latency. Option E is correct because setting minReplicaCount to 1 keeps at least one replica always provisioned and running on Vertex AI, so incoming requests hit an already-loaded model instead of triggering a new replica spin-up, eliminating the cold start entirely. Option A is wrong because setting minReplicaCount to 0 enables scale-to-zero, meaning replicas are torn down when idle and every new request incurs a full cold start, which is the opposite of the goal. Option C is wrong because increasing maxReplicaCount only raises the ceiling for horizontal scaling under load and does nothing to reduce the latency of starting an individual replica. Option D is wrong because batch prediction is an asynchronous, job-based mode that does not serve real-time online requests and therefore is irrelevant to minimizing endpoint cold-start latency.

Answer

Set minReplicaCount to 0 to allow scale-to-zero.

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Increase maxReplicaCount to a high number.

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Use batch prediction instead of online prediction.

Question 9

A retail company wants to generate product recommendations on their website using Google Cloud. They have historical transaction data and need a managed service that provides personalized recommendations like 'frequently bought together'. Which service should they use?

Accepted Answer

Recommendations AI. Recommendations AI is a purpose-built managed service on Google Cloud designed for retail recommendation use cases, including 'frequently bought together', 'recommended for you', and 'others you may like'. It ingests historical transaction and catalog data, trains retail-specific models automatically, and serves personalized recommendations via API without requiring ML expertise. This directly matches the requirement for a managed service providing personalized product recommendations.

Answer

BigQuery ML

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Vertex AI Prediction

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AutoML Tables

Question 10

A data science team is building a real-time feature engineering pipeline for ML model training and serving. They need to compute features from streaming data, store them for low-latency serving, and ensure consistency between training and serving. Which TWO Google Cloud services should they use?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store (A) is correct because it provides a centralized repository for storing, serving, and sharing feature data with low-latency online serving and batch serving for training, ensuring consistency between training and serving through point-in-time lookups and feature value time-stamping. Cloud Dataflow (D) is correct because it is a fully managed stream and batch processing service based on Apache Beam, enabling real-time feature engineering from streaming data with exactly-once processing semantics and automatic scaling.

Answer

BigQuery

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Cloud Functions

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Cloud SQL

Question 11

Drag and drop the steps to set up a distributed training job on Vertex AI using a custom container in the correct order.

Accepted Answer

Prepare training code and create Dockerfile, then build and push the container image to Artifact Registry, then configure the distributed training job on Vertex AI, then submit the job.. The correct sequence for setting up a distributed training job on Vertex AI using a custom container is to first prepare the training code and Dockerfile, then build and push the container image to a registry, then configure the job with the image URI and distributed settings, and finally submit the job. This order ensures that all dependencies are met at each step.

Answer

Build and push the container image first, then write the training code and create Dockerfile, then configure the job, then submit.

Answer

Configure the distributed training job on Vertex AI first, then prepare code and container, then push, then submit.

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Submit the job first, then build and push the container, then write code, then configure.

Question 12

A data science team uses a shared Cloud Storage bucket to store training datasets. They notice that some team members accidentally overwrite existing datasets, causing issues with reproducibility. Which approach best prevents accidental overwrites while maintaining collaboration?

Accepted Answer

Enable object versioning on the bucket and use lifecycle rules to manage versions.. Enabling object versioning on a Cloud Storage bucket preserves all versions of an object, so even if a team member overwrites a dataset, the previous version remains accessible. This maintains collaboration (anyone can upload) while preventing permanent data loss. Lifecycle rules can then be used to manage storage costs by automatically deleting old versions after a specified period.

Answer

Use a single shared service account with strict IAM roles that allow only append operations.

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Require team members to manually rename files before uploading.

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Set bucket permissions to read-only for all team members except the data owner.

Question 13

You want to use Vertex AI Vizier for hyperparameter tuning. You have 2 categorical parameters and 3 continuous parameters. Which algorithm is best suited for this mixed parameter space?

Accepted Answer

Bayesian optimization. Bayesian optimization is the default and best-suited algorithm in Vertex AI Vizier for mixed parameter spaces containing both categorical and continuous parameters. It builds a probabilistic surrogate model of the objective function and uses an acquisition function to intelligently select the next hyperparameter configuration, handling categorical and continuous dimensions natively. This makes it far more sample-efficient than random or grid search when the search space is mixed and potentially high-dimensional.

Answer

Evolutionary algorithm

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Random search

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Grid search

Question 14

A retail company wants to build a recommendation system to show 'frequently bought together' items. Which Recommendations AI model type should they use?

Accepted Answer

frequently-bought-together. The 'frequently-bought-together' model type in Google Cloud Recommendations AI is specifically designed to identify items that are commonly purchased in the same transaction, using co-occurrence analysis of historical purchase data. This directly matches the requirement to show items that are frequently bought together, leveraging association rule mining (e.g., Apriori algorithm) to generate recommendations.

Answer

recently-viewed

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recommended-for-you

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others-you-may-like

Question 15

A retail company uses Vertex AI AutoML to train a product recommendation model. They have a dataset of past purchases stored in BigQuery. The data science team wants to iteratively train and improve the model. They need to track which dataset version was used for each model and preserve the exact data for reproducibility. They currently export data to CSV files and store them in Cloud Storage. However, the dataset is updated daily, and they want to ensure that models are trained on a consistent snapshot. What should they do?

Accepted Answer

Use BigQuery snapshots to capture a versioned dataset and reference the snapshot in the training pipeline.. BigQuery snapshots provide a consistent, versioned view of the dataset at a specific point in time, ensuring reproducibility without duplicating data. By referencing the snapshot in the Vertex AI training pipeline, the team can train models on the exact same data snapshot, even as the source table is updated daily. This approach avoids the overhead of exporting to CSV and Cloud Storage while maintaining data integrity and lineage.

Answer

Use Vertex AI Dataset service to create a dataset and export it to BigQuery.

Answer

Train the model directly on the BigQuery table and let AutoML handle versioning.

Answer

Export the data to a timestamped CSV file and store it in Cloud Storage before each training run.