PMLE Practice Test 2 — 25 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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

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Manual search

Question 11

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 12

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 13

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 14

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 15

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

Question 16

A machine learning engineer wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they use?

Accepted Answer

Python function component with @dsl.component. The `@dsl.component` decorator in KFP SDK allows you to define a lightweight Python function component that runs custom code without requiring a container image. It automatically generates a container specification from the function's dependencies, making it ideal for simple, non-containerized pipeline steps.

Answer

Importer component

Answer

Container component

Answer

Vertex AI Training job

Question 17

A data scientist needs to scale a prototype deep learning model to train on a massive dataset using multiple GPUs. Which three strategies are essential for efficient distributed training? (Select THREE)

Accepted Answer

Implement data parallelism.. Option B is correct because data parallelism is the foundational strategy for multi-GPU training: the model is replicated on each GPU and each replica processes a different shard of the massive dataset, with gradients aggregated across workers to scale throughput. Option C is correct because at multi-GPU scale the input pipeline can easily starve the accelerators; using tf.data.Dataset with parallel reads (num_parallel_reads / interleave) and prefetching (prefetch(AUTOTUNE)) overlaps data loading and preprocessing with GPU computation so the GPUs are not idle. Option D is correct because synchronous gradient updates (e.g., all-reduce via NCCL, as in tf.distribute.MirroredStrategy) aggregate gradients from all workers before applying the optimizer step, which keeps replicas consistent and yields stable, reproducible convergence for deep learning training. Option A is not correct because a single large batch size is not itself an essential distributed-training strategy; batch size is a tuning choice, and naively enlarging it can hurt convergence and may not fit in memory. Option E is not correct because asynchronous gradient updates are an alternative to, not a requirement for, efficient distributed training, and they can introduce stale gradients that degrade model accuracy and reproducibility.

Answer

Use a single large batch size across all workers.

Answer

Use asynchronous gradient updates to reduce communication overhead.

Question 18

Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?

Accepted Answer

Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob. Option A is correct because a cloudbuild.yaml file is the declarative configuration Cloud Build uses to define build steps, and one of those steps can build a custom training container image (e.g., via docker build) and then submit a Vertex AI PipelineJob using the gcloud ai pipelines run command or the Vertex AI SDK. Option D is correct because Vertex AI Pipelines is the native service for orchestrating ML workflow steps such as training, evaluation, and deployment as a directed acyclic graph, and Cloud Build can invoke that pipeline as part of CI/CD, giving a fully automated, reproducible ML pipeline. Option E is correct because a Cloud Build trigger bound to the source repository (e.g., a GitHub or Cloud Source Repositories trigger on push or pull request) is what actually initiates the automated build/pipeline run on commits, which is the core of CI/CD automation. Option B is not appropriate because Cloud Functions is not the intended mechanism for retraining models on build completion; Cloud Build triggers or Pub/Sub notifications are the correct integration points, and this adds unnecessary complexity. Option C is not appropriate because polling for build artifacts with Cloud Scheduler is an anti-pattern; event-driven triggers (Cloud Build triggers or Pub/Sub) are preferred over scheduled polling for reacting to new builds.

Answer

Use Cloud Functions to retrain the model each time a build completes

Answer

Set up a Cloud Scheduler job to poll for new build artifacts

Question 19

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories (CSR) is the correct choice because it provides Git-based version control for notebooks, enabling teams to track changes, collaborate, and maintain a full version history. Vertex AI Workbench integrates natively with CSR, allowing users to clone, commit, and push notebook files directly from the JupyterLab interface, which is essential for collaborative development with revision tracking.

Answer

Artifact Registry

Answer

Cloud Storage

Answer

Data Catalog

Question 20

A data science team has trained a TensorFlow model on-premises using a large dataset. When they try to deploy the model to Vertex AI for online predictions, the deployed model fails to start with a ‘MemoryError’. The model artifact is 2 GB, and the machine type is n1-standard-4 (15 GB RAM). What is the most likely cause?

Accepted Answer

The model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading.. The model artifact is 2 GB, and loading it into memory on an n1-standard-4 machine (15 GB RAM) can still cause a MemoryError. TensorFlow models often require additional memory for graph construction, intermediate tensors, and framework overhead, which can easily exceed the available RAM, especially when the model is loaded entirely into memory before serving.

Answer

The model is stored in a regional bucket and the Vertex AI endpoint is in a different region.

Answer

The machine type does not support TensorFlow models larger than 1 GB.

Answer

The model file is corrupted or missing dependencies, causing a crash.