PMLE Practice Test 5 — 25 Questions

Question 1

A data science team uses Cloud Composer to orchestrate ML workflows. They need to trigger a Vertex AI pipeline after a BigQuery data load completes, and then run a Dataflow job. Which Airflow operator should they use to launch the Vertex AI pipeline?

Accepted Answer

VertexAIPipelineJobOperator. Option D is correct because the VertexAIPipelineJobOperator is specifically designed to trigger a Vertex AI pipeline run from within an Airflow DAG. This operator directly corresponds to the requirement of launching a Vertex AI pipeline after a BigQuery data load completes, making it the appropriate choice for orchestrating ML workflows with Cloud Composer.

Answer

DataflowStartFlexTemplateJobOperator

Answer

VertexAICreateCustomJobOperator

Answer

MLEngineTrainingOperator

Question 2

A retail company wants to forecast weekly sales for each of its 500 stores. The data includes historical sales, promotions, holidays, and local weather. The company needs to update forecasts every week with new data. Which ML approach should they use?

Accepted Answer

Use Vertex AI Forecasting to train a time-series model with holiday and weather features. Vertex AI Forecasting is purpose-built for time-series forecasting with support for exogenous features like holidays and weather, making it the ideal choice for weekly sales predictions across 500 stores. It handles multiple time series automatically and integrates with the required weekly retraining cycle, unlike generic regression models that lack temporal awareness.

Answer

Use BigQuery ML to create a linear regression model on historical data

Answer

Export data to AutoML Tables and train a regression model

Answer

Build a custom LSTM model using TensorFlow on Vertex AI Workbench

Question 3

A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

Accepted Answer

Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.. Option D is correct because BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without requiring manual feature engineering, aligning with the goal of minimizing low-code ML effort. This approach leverages BigQuery's native SQL interface and scales automatically, making it ideal for the described e-commerce scenario.

Answer

Use a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.

Answer

Write a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.

Answer

Export the data to CSV and use AutoML Tables to train a recommendation model.

Question 4

A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this failure?

Accepted Answer

Add retry logic with exponential backoff to the deployment step in the pipeline.. Option B is correct because adding retry logic with exponential backoff handles transient quota errors gracefully without manual intervention, making it a resilient and long-term solution for intermittent failures. Option A is wrong because longer wait times do not address the root cause of quota exhaustion. Option C is wrong because deploying on Compute Engine introduces additional complexity and does not resolve Vertex AI quota issues. Option D is wrong because requesting a permanent quota increase may be necessary for sustained high volume, but it requires planning and approval, and is not the best first step for intermittent errors.

Answer

Run the pipeline steps sequentially with longer wait times.

Answer

Switch to deploying models using a custom container on Compute Engine.

Answer

Request a permanent quota increase for Vertex AI Endpoints.

Question 5

A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?

Accepted Answer

Deploy the model to a Vertex AI Endpoint directly from Model Garden.. Option D is correct because deploying directly to a Vertex AI Endpoint from Model Garden eliminates all infrastructure management. Vertex AI handles model serving, scaling, and monitoring automatically, which is ideal for a team with no experience in model serving infrastructure. This is a fully managed, serverless deployment that requires no containerization or server configuration.

Answer

Deploy the model as a Cloud Function invoked by Cloud Storage events.

Answer

Deploy the model as a Cloud Run service using a custom Docker container.

Answer

Deploy the model on App Engine flexible environment.

Question 6

A team wants to share a trained model with other teams within the organization. They need to provide access to the model artifact in Vertex AI Model Registry and ensure that only authorized teams can deploy the model. What should they do?

Accepted Answer

Use IAM to grant the 'aiplatform.models.deploy' role to the other teams on the model resource. Option D is correct because Vertex AI Model Registry uses IAM to control access to model resources. By granting the 'aiplatform.models.deploy' role on the specific model resource, you ensure that only authorized teams can deploy the model, while other operations (like viewing or updating) remain restricted. This follows the principle of least privilege and avoids exposing the model artifact broadly.

Answer

Grant the other teams access to the Cloud Storage bucket where the model is stored

Answer

Set the model to public in Vertex AI Model Registry

Answer

Use Cloud Key Management Service to encrypt the model and share the decryption key

Question 7

An ML team uses Vertex AI Pipelines to automate model retraining. The pipeline includes a step that queries BigQuery to create a training dataset. The team notices that the pipeline fails intermittently with a '403 Exceeded rate limits' error. What is the most likely cause and solution?

Accepted Answer

The pipeline is issuing too many concurrent queries; use a BigQuery reservation to guarantee slot capacity. The 403 'Exceeded rate limits' error in BigQuery indicates that the project is hitting the concurrent query rate limit or the rate of bytes read per second. Using a BigQuery reservation guarantees dedicated slot capacity, which prevents rate-limit errors by ensuring the pipeline has consistent compute resources regardless of other workloads in the project. This is the most direct solution because rate limits are enforced at the project level based on available slots, and a reservation provides a fixed number of slots that bypass those limits.

Answer

The training dataset is too large; partition the table and query only the latest partition

Answer

The pipeline step timeout is too short; increase the timeout to 30 minutes

Answer

The SQL query is inefficient; rewrite it using materialized views

Question 8

Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Accepted Answer

Retrain the model using the most recent data. Option C is correct because retraining the model on the most recent data directly addresses the distribution shift by adapting the model to the new data patterns. This is a standard practice in MLOps when the shift is confirmed and the cause is understood, ensuring the model remains accurate and reliable in production.

Answer

Immediately roll back to the previous model version

Answer

Increase logging for future predictions

Answer

Reduce the traffic to the model to minimize impact

Question 9

Your team has deployed a text classification model on Vertex AI Endpoints. You notice that the model's latency has increased significantly over the last week, but the request rate has remained stable. Which of the following is the most likely cause?

Accepted Answer

A change in the preprocessing logic that now includes a computationally expensive step. A computationally expensive preprocessing step directly increases per-request latency on the inference path, even when request rate is stable. Vertex AI Endpoints execute user-provided preprocessing code before model inference, so adding a heavy operation (e.g., large regex, image resizing, or external API call) will linearly increase response time for every prediction.

Answer

A sudden increase in the number of prediction requests

Answer

The model was replaced with a larger version without updating the endpoint

Answer

A misconfiguration in the autoscaling policy

Question 10

You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?

Accepted Answer

A recent code change that loads the entire dataset into memory before processing. Option A is correct because a code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.

Answer

Increase in model size due to retraining

Answer

Decrease in the number of worker machines

Answer

Increase in input data size

Question 11

A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?

Accepted Answer

Copy the new_data table to the US region using the BigQuery UI or CLI. Option B is correct because the simplest fix is to move the new_data table to the same region as the model (US). BigQuery ML requires that the model and the data used for predictions reside in the same multi-region or regional location. Copying the table via the BigQuery UI or CLI (e.g., `bq cp`) is a straightforward, no-code operation that avoids retraining or exporting the model.

Answer

Recreate the model in the EU region using the same training data

Answer

Enable cross-region query in BigQuery settings

Answer

Export the model from US and import it to EU

Question 12

An e-commerce company uses a recommendation model deployed on Vertex AI Endpoints. The model's latency increases gradually over two weeks, causing timeouts. The model is served using a custom container. What is the most likely root cause and corrective action?

Accepted Answer

The custom container has a memory leak; implement memory monitoring and set container resource limits.. A gradual increase in latency over two weeks, without a sudden spike, strongly indicates a memory leak in the custom container. As the leak accumulates, the container's garbage collection becomes less effective, leading to increased GC pauses and eventual timeouts. Setting resource limits and monitoring memory usage can prevent the container from exhausting host memory and causing performance degradation.

Answer

The model is receiving more traffic; scale the number of replicas.

Answer

The Vertex AI endpoint has changed its URL; update the client application.

Answer

The model file size has grown due to feature engineering; reduce feature count.

Question 13

An ML engineer is using Vertex AI Pipelines with Kubeflow Pipelines SDK (KFP) to orchestrate a training and deployment workflow. They want to reuse a custom component across multiple pipelines. The component is defined in a Python file 'preprocess.py' that includes a function decorated with @kfp.components.create_component_from_func. How should they package this component for reuse?

Accepted Answer

Import the preprocess module and call create_component_from_func on the function, then use the resulting component in pipeline definitions.. Option A is correct because the recommended way to reuse a custom component defined via `@kfp.components.create_component_from_func` is to import the Python module containing the decorated function and call `create_component_from_func` on that function in each pipeline definition. This creates a reusable component object that can be used directly in the pipeline's `@dsl.pipeline` definition without additional packaging steps. The KFP SDK treats the function as the source of truth, and re-importing ensures the component logic is always current.

Answer

Save the component as a YAML file using kfp.components.ComponentStore and load it in other pipelines.

Answer

Compile the pipeline that uses the component into a JSON file and upload it to Vertex AI.

Answer

Build a custom container image with the function and use it as a base image in other pipelines.

Question 14

You are an ML engineer at a large e-commerce company. Your team has developed a product recommendation model using TensorFlow and deployed it on Vertex AI Endpoints for real-time inference. The model is retrained weekly using a Vertex AI Pipeline that reads new user interaction data from BigQuery, trains the model, evaluates it, and deploys the new version to the endpoint with a traffic split: 10% to the new model and 90% to the previous champion model. Recently, the team noticed that the new model's online prediction latency has increased significantly (from 50ms to 200ms) after deployment, causing timeouts for some requests. The training code has not changed, and the model size is similar. The pipeline uses a custom container with the same TensorFlow Serving image as before. The deployment step uses the same machine type (n1-standard-4) for the endpoint. What is the most likely cause of the latency increase?

Accepted Answer

The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation.. Option C is correct because the pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation. This additional operation runs during inference on Vertex AI Endpoints, increasing the per-request latency from 50ms to 200ms, even though the model architecture and size remain unchanged. The custom container and machine type are identical, so the latency increase must stem from a change in the serving graph itself.

Answer

The endpoint is using a machine type that is not optimized for the new model's computation.

Answer

The new model has a significantly different architecture that requires more computation.

Answer

The new model is experiencing data skew because the training data distribution has changed.

Question 15

A company implements an ML pipeline using Vertex AI Pipelines. The pipeline trains a model using custom training jobs and then deploys it to an endpoint. The team notices that the endpoint occasionally serves an older model version for a few minutes after a new pipeline run completes. What is the most likely cause?

Accepted Answer

The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.. D is correct because Vertex AI endpoints can be configured with a canary (gradual) traffic rollout strategy. When a new model is deployed, traffic is shifted incrementally from the old model to the new one over a specified duration. During this rollout window, the old model continues to serve a fraction of requests, which explains why users occasionally see the older model version for a few minutes after the pipeline completes.

Answer

The new model artifact is temporarily unavailable, so the endpoint falls back to the previous version.

Answer

The prediction cache is returning cached results from the old model.

Answer

The pipeline failed to update the endpoint with the new model ID.

Question 16

A team of data scientists and ML engineers is collaborating on a project using Vertex AI Workbench. They need to share notebooks and code, but want to avoid conflicts and maintain a history of changes. Which approach should they use?

Accepted Answer

Use a git repository (e.g., Cloud Source Repositories) to manage code and notebooks.. Option D is correct because using a git repository (e.g., Cloud Source Repositories) provides version control, branching, and a full history of changes, which is essential for collaborative development. This approach avoids conflicts by allowing team members to work on separate branches and merge changes systematically, unlike shared storage or manual methods that lack conflict resolution and audit trails.

Answer

Email notebook files to each other and manually merge changes.

Answer

Store notebooks in a shared Cloud Storage bucket and access them simultaneously.

Answer

Use Vertex AI Experiments to share notebook outputs.

Question 17

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.. Option D is correct because 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.

Answer

Require team members to manually rename files before uploading.

Answer

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

Question 18

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.. Option B is correct because 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 19

A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the correct feature because it captures parameters, metrics, and artifacts for each pipeline run, enabling reproducibility and lineage tracking. This directly supports the team's need to ensure runs are reproducible and artifacts are tracked, as Experiments automatically logs metadata for every execution.

Answer

Vertex AI Feature Store

Answer

Vertex AI Model Registry

Answer

Vertex AI Endpoints

Question 20

A machine learning team has a prototype using a custom TensorFlow model trained on a small dataset stored in Cloud Storage. They want to scale the prototype to production with minimal code changes while ensuring the model can handle increased traffic and new data. The model currently loads data using tf.data.Dataset from CSV files. Which approach best meets these requirements?

Accepted Answer

Use Vertex AI Training with hyperparameter tuning and distributed training, then deploy the model to Vertex AI Prediction with autoscaling.. Vertex AI Prediction with autoscaling directly addresses the need to handle increased traffic without code changes, while Vertex AI Training with hyperparameter tuning and distributed training enables scaling to larger datasets with minimal modifications to the existing tf.data pipeline. This approach keeps the custom TensorFlow model intact and leverages managed infrastructure for both training and serving.

Answer

Deploy the model to AI Platform (Unified) Prediction with a custom container, and use AI Platform Training to retrain on larger datasets.

Answer

Migrate the model to BigQuery ML and use SQL for training and prediction to leverage BigQuery's scalability.

Answer

Package the model as a Cloud Run Function and use Cloud Scheduler to trigger retraining periodically.