PMLE Practice Test 2 — 30 Questions

Question 1

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.

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Kubeflow Pipelines

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Custom TensorFlow model

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BigQuery ML

Question 2

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.

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Use Cloud Composer to orchestrate preprocessing

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Write shared Python packages in Artifact Registry

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Use Cloud Dataflow templates

Question 3

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.

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

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

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

Question 4

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.

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

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

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

Question 5

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.

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Random search

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Grid search

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Manual search

Question 6

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.

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

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Use Vertex AI Experiments to compare distributions.

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

Question 7

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.

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

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

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

Question 8

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.

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Use Cloud Load Balancing with weighted backend services.

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Use Vertex AI Experiments to manage model versions.

Question 9

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 10

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.

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

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

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

Question 11

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.

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Importer component

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Container component

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Vertex AI Training job

Question 12

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 13

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

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

Question 14

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.

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Artifact Registry

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Cloud Storage

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Data Catalog

Question 15

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.

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

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

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

Question 16

Refer to the exhibit. An alert policy is configured to trigger when prediction latency exceeds 500 ms for 5 consecutive minutes. The team is experiencing many false positive alerts during brief latency spikes. Which adjustment would most effectively reduce false positives while still detecting prolonged latency issues?

Accepted Answer

Increase the duration to 30 minutes. Increasing the duration from 5 to 30 minutes (Option C) directly addresses the problem of false positives from brief latency spikes by requiring the latency to exceed 500 ms for a longer continuous period before triggering an alert. This ensures that only sustained, prolonged latency issues—not transient spikes—activate the policy, aligning with the goal of detecting genuine degradation while ignoring noise.

Answer

Change the comparison to less than

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Add a condition that CPU utilization is also high

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Increase the threshold to 1000 ms

Question 17

A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

Accepted Answer

Delta Lake on Dataproc. Delta Lake on Dataproc provides ACID transactions on cloud storage data lakes, enabling concurrent reads/writes with consistency.

Answer

BigQuery table snapshots

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DVC (Data Version Control)

Answer

Vertex AI Feature Store

Question 18

An ML pipeline must run a set of preprocessing tasks for each data shard in parallel. Which KFP SDK features should they use to implement this? (Choose two.)

Accepted Answer

dsl.ParallelFor. dsl.ParallelFor (A) is correct because it is the KFP SDK construct that iterates over a list (such as the set of data shards) and fans out the loop body into parallel task executions, which is exactly what is needed to run preprocessing per shard concurrently. dsl.Collected (C) is correct because it is used with dsl.ParallelFor to gather the outputs of all parallel iterations into a single list, allowing downstream pipeline steps to consume the aggregated results of the per-shard preprocessing. dsl.PipelineParam (B) is not the mechanism for parallel iteration; it only represents a runtime parameter passed into a pipeline or component. dsl.Condition (D) implements conditional branching (if/else) rather than parallel fan-out, and dsl.ExitHandler (E) defines cleanup logic that runs when a scope exits, neither of which provides the required parallel execution over shards.

Answer

dsl.PipelineParam

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dsl.Condition

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dsl.ExitHandler

Question 19

A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is the correct choice because it allows users with no prior ML experience to train a custom image classification model using a simple graphical interface, requiring only labeled images as input. It automates model architecture search, hyperparameter tuning, and deployment, minimizing manual effort while delivering a production-ready model.

Answer

Cloud TPU

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Vertex AI Workbench with a custom TensorFlow model

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Vision API

Question 20

Match each MLOps practice to its description.

Accepted Answer

CI/CD: Automates the processes of building, testing, and deploying ML models.. This matching question requires associating each MLOps practice (CI/CD, Model Monitoring, Data Versioning) with its correct description. The correct matches are: CI/CD automates building, testing, and deploying ML models (option A); Model Monitoring continuously observes model performance and detects data drift (option C); Data Versioning manages and tracks changes to datasets over time (option E). Common mistakes include confusing CI/CD with monitoring or versioning, or assigning model monitoring to data versioning tasks. The wrong options (B, D, F) are incorrect because they swap responsibilities: B wrongly attributes monitoring to CI/CD, D assigns versioning to monitoring, and F assigns CI/CD to versioning.

Answer

CI/CD: Continuously observes model performance and detects data drift.

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Model Monitoring: Manages and tracks changes to datasets over time.

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Data Versioning: Automates the processes of building, testing, and deploying ML models.