PMLE Practice Exam 2 — 20 Questions

Question 1

An MLOps team needs to automatically retrain a model when new training data becomes available. They use Vertex AI Pipelines. What is the recommended way to trigger the pipeline?

Accepted Answer

Cloud Functions triggered by Cloud Storage events. Vertex AI Pipelines does not natively support event-driven triggers. The recommended pattern is to use Cloud Functions, which can be triggered by Cloud Storage events (e.g., object finalize/create) when new training data is uploaded. The Cloud Function then programmatically submits the pipeline run via the Vertex AI Pipelines client library or REST API, enabling an automated retraining workflow.

Answer

Use Model Evaluation to decide

Answer

Set up a trigger in Vertex AI Pipelines

Answer

Cloud Scheduler on a daily basis

Question 2

A financial institution needs to extract structured data from scanned PDFs of loan applications, including text fields and tables. They require a human review step for high-risk applications. Which Google Cloud service and configuration should they use?

Accepted Answer

Document AI with a form parser processor and enable Human-in-the-Loop for high-risk applications. Document AI's Form Parser processor is purpose-built to extract structured key-value pairs and tables from forms like loan applications, and it natively integrates with Human-in-the-Loop (HITL) to route low-confidence or high-risk documents to human reviewers. This combination directly satisfies both the extraction and human review requirements without custom code.

Answer

Document AI with a custom extractor processor and use Cloud Functions for human review

Answer

Cloud Vision API to detect text and tables, then send to Cloud Dataflow for processing

Answer

Vertex AI AutoML Vision to train a custom model for document parsing

Question 3

A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?

Accepted Answer

Store the training code in a Cloud Source Repository and tag commits with the experiment ID.. Option A is correct because storing the training code in Cloud Source Repositories and tagging commits with the experiment ID creates an immutable, versioned link between the exact code revision and the Vertex AI Experiment run, which is essential for reproducibility. Option B is correct because packaging the training environment in a custom container image pushed to Artifact Registry with a fixed (immutable) tag guarantees every team member runs the identical dependencies, libraries, and runtime, eliminating environment drift. Option C is correct because logging the dataset path and version as experiment parameters in Vertex AI Experiments captures the exact data snapshot used, so the same inputs can be retrieved and reused. Option D is not appropriate because sharing a service account key is an insecure credential-management anti-pattern and does not itself contribute to code, data, or environment reproducibility. Option E is not appropriate because hyperparameter tuning optimizes model performance and does not by itself ensure that code, data, and environment are pinned and reproducible.

Answer

Share a service account key with all team members so they can access the same resources.

Answer

Use Vertex AI's hyperparameter tuning job to automatically find the best parameters.

Question 4

Which THREE practices improve collaboration when using Cloud Composer for ML pipelines?

Accepted Answer

Use a shared Cloud Storage bucket for intermediate artifacts with appropriate permissions.. Cloud Composer workflows often require sharing intermediate data (e.g., transformed datasets, model checkpoints) across multiple DAGs or team members. A shared Cloud Storage bucket with fine-grained IAM permissions enables secure, centralized artifact exchange without duplicating data or exposing it to unauthorized users. This practice avoids hard-coded paths and ensures that all pipeline stages can reliably access the same artifacts, which is critical for reproducibility and collaboration in ML pipelines.

Answer

Keep all pipeline logic in a single large DAG for simplicity.

Answer

Embed service account keys directly in DAG code for authentication.

Question 5

You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?

Accepted Answer

Write the data to Cloud Storage and pass the GCS URI as an artifact.. In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.

Answer

Use the component's temporary directory to share data between containers.

Answer

Pass the data as a serialized Python object in memory.

Answer

Store the data in a BigQuery table and pass the table reference.

Question 6

A company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?

Accepted Answer

Deploy the new version as a separate model on the same endpoint and use the `traffic_split` parameter in the deployment request.. Vertex AI endpoints support traffic splitting between multiple deployed models. By deploying the new model version to the same endpoint and setting `traffic_split` to 5% for the new version and 95% for the existing version, the endpoint automatically routes a corresponding proportion of inference requests to each model without any client-side changes.

Answer

Create a new endpoint for the new version and update the client to call both endpoints.

Answer

Deploy the new version and set the minimum replicas to 0, then gradually increase.

Answer

Use Cloud Load Balancing to distribute traffic between two endpoints.

Question 7

An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?

Accepted Answer

Use the Natural Language API. The Natural Language API provides pre-built sentiment analysis with minimal setup. AutoML NLP would require custom training, BigQuery ML is for tabular data, and Vertex AI Prediction needs a deployed model.

Answer

Use Vertex AI Prediction with a pre-trained model

Answer

Use BigQuery ML with LOGISTIC_REG

Answer

Train a custom model using AutoML NLP

Question 8

A data scientist wants to perform A/B testing between two model versions deployed on the same Vertex AI endpoint. They need to route 10% of traffic to the challenger model. Which approach should they use?

Accepted Answer

Deploy both models to the same endpoint and set traffic_split to 90 for champion and 10 for challenger. Vertex AI endpoints support traffic splitting directly, allowing you to route a percentage of requests to different model versions deployed on the same endpoint. By setting `traffic_split` to 90 for the champion and 10 for the challenger, the data scientist can perform online A/B testing without additional infrastructure. This is the simplest and most cost-effective approach, as it avoids managing separate endpoints or load balancers.

Answer

Use Vertex AI Experiments to compare models offline, then deploy the winner

Answer

Deploy the challenger model to a separate endpoint and use a load balancer to split traffic

Answer

Update the champion model with a new version and use model version aliases

Question 9

You want to use a pre-trained model from TensorFlow Hub for image classification, but you need to adapt it to classify your own custom categories with a small dataset. Which Vertex AI approach is most appropriate?

Accepted Answer

Write a custom training script that loads the pre-trained model and fine-tunes it on your dataset. Fine-tuning a pre-trained model on your custom dataset is the most appropriate approach when you have a small dataset and need to adapt the model to new categories. This leverages transfer learning, where the pre-trained weights are used as a starting point and updated with your data.

Answer

Deploy the pre-trained model as-is via Vertex AI JumpStart

Answer

Build a custom container with the pre-trained model and deploy to Vertex AI Endpoints

Answer

Use Vertex AI AutoML for image classification

Question 10

A data engineer wants to use BigQuery ML to train a model for predicting customer churn (binary classification) using a large dataset. They want the model to be automatically tuned. Which model type should they choose?

Accepted Answer

AUTOML_CLASSIFIER. (AUTOML_CLASSIFIER) is correct because it automatically performs architecture search and hyperparameter tuning to find the best model for binary classification tasks, such as customer churn prediction. This is ideal when the data engineer wants the model to be automatically tuned without manual intervention, as AutoML handles feature engineering, model selection, and tuning under the hood.

Answer

LOGISTIC_REG

Answer

BOOSTED_TREE_CLASSIFIER

Answer

DNN_CLASSIFIER

Question 11

A company wants to predict customer churn using a dataset with 10,000 rows and 20 features. They have no ML expertise. Which low-code solution should they use?

Accepted Answer

Vertex AI AutoML Tables. Vertex AI AutoML Tables is the correct low-code solution because it allows users with no ML expertise to train high-quality tabular models on structured data (10,000 rows, 20 features) without writing any code. It automates feature engineering, model selection, and hyperparameter tuning, and provides a simple UI to upload data and get predictions. This directly matches the requirement of a low-code, no-expertise solution for a tabular churn prediction problem.

Answer

Kubeflow Pipelines

Answer

Custom TensorFlow model

Answer

BigQuery ML

Question 12

A company has multiple teams working on different models. They want to enforce consistent data preprocessing steps across all teams. Which approach should they take?

Accepted Answer

Create shared Vertex AI Pipelines components. Vertex AI Pipelines components allow teams to define reusable, versioned, and parameterized preprocessing steps that can be shared across models and pipelines. This ensures consistent execution of data transformations because each component encapsulates the exact code and environment, and pipelines enforce the same DAG of steps regardless of which team triggers them.

Answer

Use Cloud Composer to orchestrate preprocessing

Answer

Write shared Python packages in Artifact Registry

Answer

Use Cloud Dataflow templates

Question 13

A data engineering team needs to compute rolling window features (7-day average, 30-day sum) from a high-volume stream of e-commerce events stored in BigQuery. They must output the features to Vertex AI Feature Store for online serving. Which approach is MOST cost-effective and scalable?

Accepted Answer

Use Dataflow with Apache Beam, reading from BigQuery, computing windowed aggregations, and writing to Vertex AI Feature Store. Dataflow (Apache Beam) is ideal for processing large-scale batch and streaming data. It can read from BigQuery, perform windowing computations, and write to Feature Store's online store. Cloud Functions have timeouts, Cloud Composer is not optimal for streaming, and BigQuery scheduled queries are not designed for streaming-feature computation.

Answer

Use Cloud Composer (Airflow) with a daily DAG to run SQL queries on BigQuery and export results

Answer

Schedule a query in BigQuery using scheduled queries and export results to Feature Store

Answer

Use Cloud Functions triggered by Pub/Sub to compute features on the fly

Question 14

A financial services company has deployed a credit risk ML model on Vertex AI. They want to monitor the model for fairness across demographic groups to ensure no biased outcomes. Which TWO actions should they take as best practices? (Choose TWO.)

Accepted Answer

Use Vertex Explainable AI to understand feature attributions and compare their distributions across demographic groups.. Vertex Explainable AI provides feature attribution scores that can be compared across demographic groups to detect if the model relies on sensitive attributes or proxies. This enables fairness auditing by revealing whether the model's decision logic differs systematically for protected groups, which is a best practice for monitoring bias.

Answer

Eliminate all features that are correlated with protected attributes from the model input to ensure fairness.

Answer

Periodically compare the model's performance metrics (e.g., AUC) on the overall population versus the holdout test set.

Answer

Store all model predictions in BigQuery but do not capture ground truth labels to avoid privacy issues.

Question 15

An ML team wants to use Vertex AI Hyperparameter Tuning to tune a custom training job. They have a budget of 50 trials and want to use an algorithm that balances exploration and exploitation. Which algorithm should they choose?

Accepted Answer

Bayesian optimization (Vizier default). Bayesian optimization (the default algorithm in Vertex AI Vizier) is the correct choice because it explicitly balances exploration and exploitation by building a probabilistic model of the objective function and using an acquisition function to select the next hyperparameter configuration. With a budget of 50 trials, this algorithm efficiently converges to optimal regions while still exploring uncertain areas, making it ideal for tuning custom training jobs where each trial is computationally expensive.

Answer

Random search

Answer

Grid search

Answer

Manual search

Question 16

A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?

Accepted Answer

Enable feature monitoring on the feature group with drift threshold and notification channel.. Feature monitoring in Vertex AI Feature Store allows defining drift thresholds and alerting via Cloud Monitoring.

Answer

Use Cloud Monitoring custom metrics and log-based alerts manually.

Answer

Use Vertex AI Experiments to compare distributions.

Answer

Export features to BigQuery and set up scheduled queries with alerts.

Question 17

You deploy a new version of a model to a Vertex AI endpoint and want to gradually shift traffic from the old version to the new version over 24 hours. The endpoint currently serves 100% traffic to the old version. What should you do?

Accepted Answer

Update the endpoint to split traffic between the two model versions using the traffic split configuration.. Vertex AI endpoints support a built-in traffic split configuration that allows you to gradually shift traffic between model versions deployed to the same endpoint. By updating the endpoint's traffic split percentages (e.g., from 100% old / 0% new to 0% old / 100% new over 24 hours), you can achieve a smooth, controlled rollout without changing client code or managing multiple endpoints.

Answer

Use Vertex AI Experiments to run an A/B test between the two versions.

Answer

Deploy the new version to a separate endpoint and update your client to use the new endpoint for a percentage of requests.

Answer

Delete the old version and redeploy the new version with a different endpoint name, then update DNS.

Question 18

A data science team needs to serve multiple versions of the same ML model on Vertex AI Endpoints for A/B testing. They want to gradually shift traffic from the current 'champion' model to a new 'challenger' model. Which feature should they use?

Accepted Answer

Deploy both models to the same endpoint and use traffic splitting.. Vertex AI Endpoints natively support traffic splitting, allowing you to deploy multiple model versions (e.g., champion and challenger) to the same endpoint and assign a percentage of traffic to each. This enables gradual A/B testing without additional infrastructure, as the endpoint automatically routes requests based on the configured split. Option C is correct because it leverages this built-in feature, which is designed specifically for this use case.

Answer

Deploy the challenger to a separate endpoint and use a proxy to split traffic.

Answer

Use Cloud Load Balancing with weighted backend services.

Answer

Use Vertex AI Experiments to manage model versions.

Question 19

An ML engineer is building a monitoring dashboard for a Vertex AI pipeline that includes training, evaluation, and batch prediction. Which THREE components should be included to provide comprehensive observability? (Select THREE.)

Accepted Answer

Pipeline execution status, duration, and failure rates for each component.. Option A is correct because tracking pipeline execution status, duration, and failure rates per component gives the operational observability needed to detect where a training, evaluation, or batch prediction step fails or slows down. Option C is correct because model evaluation metrics such as accuracy and AUC after training and validation are essential to monitor model quality and detect degradation before deployment. Option D is correct because data validation reports with anomaly counts and feature statistics surface input data drift, skew, and schema issues that directly affect pipeline and model reliability. Option B is not appropriate because raw Compute Engine CPU and memory logs for each step are low-level infrastructure telemetry, not the pipeline-level observability components the scenario requires. Option E is not appropriate because online prediction latency and request counts apply to a deployed endpoint, whereas this pipeline uses batch prediction and has no online serving endpoint.

Answer

Compute engine CPU and memory logs for each pipeline step.

Answer

Online prediction latency and request count from the deployed model endpoint.

Question 20

Drag and drop the steps to set up a BigQuery ML linear regression model for forecasting in the correct order.

Accepted Answer

Prepare training data → Create model → Evaluate model → Use model for predictions. For BigQuery ML linear regression, the correct order is: 1) Prepare training data by selecting and preprocessing features in a SQL query; 2) Create the model using `CREATE MODEL` with the training data; 3) Evaluate the model using `ML.EVALUATE` to check metrics like R²; 4) Use the model for predictions with `ML.PREDICT`. This sequence ensures the model is built on clean data, validated, and then applied.

Answer

Prepare training data → Create model → Use model for predictions → Evaluate model

Answer

Create model → Prepare training data → Evaluate model → Use model for predictions

Answer

Prepare training data → Evaluate model → Create model → Use model for predictions