PMLE Practice Test 5 — 25 Questions

Question 1

An MLOps team has deployed a model on Vertex AI Endpoints and wants to monitor for skew between training and serving data distributions. Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the service designed to monitor for skew and drift between training and serving data distributions. It can detect anomalies in feature distributions and alert when skew or drift exceeds thresholds.

Answer

Vertex AI Explainability

Answer

Vertex AI Model Registry

Answer

Vertex AI Continuous Training

Question 2

A company wants to automatically retrain their model every night at 2 AM using Vertex AI Pipelines. Which approach should they use to trigger the pipeline on a schedule?

Accepted Answer

Use Cloud Scheduler to call the Vertex AI pipeline creation API. Cloud Scheduler is the correct approach because it can directly invoke the Vertex AI Pipeline creation API via an HTTP trigger at a specified cron schedule (e.g., 2 AM daily). This integrates natively with Vertex AI's pipeline orchestration, allowing the scheduler to submit a pipeline run without additional infrastructure. The other options either lack native Vertex AI pipeline support or introduce unnecessary complexity.

Answer

Deploy the pipeline as a Cloud Run job with a cron trigger

Answer

Use Vertex AI Experiments to schedule runs

Answer

Configure a cron job inside the pipeline definition

Question 3

A data scientist wants to perform feature engineering on a large dataset stored in BigQuery before training a model. Which feature engineering tool is most appropriate?

Accepted Answer

Use BigQuery ML TRANSFORM clause. BigQuery ML's TRANSFORM clause allows feature engineering to be performed directly within BigQuery using SQL, eliminating the need to export data or build separate pipelines. It automatically applies transformations during model training and prediction, ensuring consistency and efficiency for large datasets. This is the most integrated and appropriate tool for feature engineering on BigQuery data.

Answer

Use Vertex AI Feature Store to store engineered features

Answer

Export data to Cloud Dataproc for feature engineering

Answer

Create a Dataflow pipeline to compute features

Question 4

What is the primary benefit of using a centralised model registry in MLOps?

Accepted Answer

Governance and version control of models. A centralised model registry provides governance, versioning, and lineage tracking, enabling collaboration and auditability.

Answer

Better hyperparameter tuning

Answer

Faster model training

Answer

Automatic model deployment

Question 5

A data scientist has deployed a model with Vertex AI Endpoints and enabled request/response logging to BigQuery. They want to compute a confusion matrix over time to monitor model quality. What should they do?

Accepted Answer

Upload ground truth labels to BigQuery and join with prediction logs, then compute confusion matrix in a scheduled query. To compute a confusion matrix over time for a deployed model on Vertex AI Endpoints with request/response logging to BigQuery, you need ground truth labels. Vertex AI does not automatically generate confusion matrices from prediction logs alone. You must upload the actual labels (ground truth) to BigQuery and join them with the prediction logs, then compute the confusion matrix using a scheduled query. This allows you to monitor model quality over time. Vertex AI Model Monitoring (A) can detect skew and drift but does not automatically generate confusion matrices. Cloud Monitoring (B) is for infrastructure and application metrics, not model quality. Explainability (D) provides feature attributions, not confusion matrices.

Answer

Use Vertex AI Model Monitoring to automatically generate confusion matrices

Answer

Use Cloud Monitoring to create a confusion matrix dashboard

Answer

Enable Vertex AI Explainability to get confusion matrix

Question 6

A team uses Vertex AI Workbench managed notebooks. They want to version control their notebook files and collaborate using Git. What is the best way to integrate Git?

Accepted Answer

Use the built-in Git integration in Vertex AI Workbench managed notebooks. Vertex AI Workbench managed notebooks include native Git integration through the JupyterLab Git extension, allowing users to clone repositories, commit, push, and pull directly from the notebook UI without leaving the environment. This is the officially supported and most seamless method for version-controlling notebooks in managed notebooks, supporting GitHub, Cloud Source Repositories, and any Git-compatible remote.

Answer

Use Cloud Source Repositories only

Answer

Use gcloud source repos clone inside the terminal

Answer

Manually download notebooks and upload to GitHub via browser

Question 7

A company has a large-scale ML system that uses Vertex AI Pipelines to retrain models weekly. The pipeline includes a custom training job and a batch prediction step. After moving to production, they observe that batch prediction jobs often fail with 'Quota exceeded' errors. The project has sufficient CPU quota. What is the most likely cause?

Accepted Answer

The batch prediction job is requesting a specific accelerator type that has a separate quota limit.. The most likely cause is that the batch prediction job is requesting a specific accelerator type (e.g., GPU or TPU) that has a separate quota limit from CPU quota. In Vertex AI, accelerator quotas are distinct from general compute (CPU) quotas, and even if the project has sufficient CPU quota, the accelerator quota may be exhausted, causing 'Quota exceeded' errors.

Answer

The pipeline is exceeding the maximum number of concurrent pipeline runs.

Answer

The batch prediction job is using a machine type that is not available in the region.

Answer

The custom training job is consuming all available quota before the batch prediction job starts.

Question 8

A team is troubleshooting a Vertex AI Pipelines run that keeps failing at the model evaluation step. The pipeline includes steps: data preprocessing, training, evaluation, and deployment. Which THREE actions should they take to diagnose the issue?

Accepted Answer

Verify that the training step output is correctly linked as input to evaluation.. Option A is correct because Vertex AI Pipelines passes data between steps through declared input/output artifacts, so if the training step's output artifact is not correctly wired as the evaluation step's input, the evaluation component will fail from missing or malformed input. Option B is correct because running the evaluation code locally with the same input data isolates the component logic from the pipeline orchestration, quickly revealing whether the failure is in the code or in the pipeline configuration. Option D is correct because Cloud Logging captures the evaluation step's container stdout/stderr and error messages, which is the primary way to see the actual exception raised during the pipeline run. Option C is not appropriate because increasing memory is a speculative resource change that does not diagnose the root cause and would only help if an out-of-memory error were already confirmed. Option E is not appropriate because swapping the evaluation step for Vertex AI Model Evaluation changes the pipeline rather than diagnosing the existing failure, and it may not even be compatible with the current pipeline's inputs and outputs.

Answer

Increase the memory of the evaluation step's machine.

Answer

Replace the evaluation step with a Vertex AI Model Evaluation service.

Question 9

A machine learning engineer is building a pipeline with Vertex AI Pipelines and wants to pass a large dataset between components without copying it to the container's memory. What is the best practice for passing data between pipeline components?

Accepted Answer

Use Cloud Storage URIs (gs://) to point to the data location.. Vertex AI Pipelines natively supports passing Cloud Storage URIs (gs://) as artifact references between components, allowing components to read the dataset directly from GCS without copying it into container memory. This avoids memory limits and enables efficient handling of large datasets by leveraging GCS's scalable object storage.

Answer

Mount an NFS volume to all containers and share data via the filesystem.

Answer

Serialize the dataset to JSON and include it as a pipeline parameter.

Answer

Use the importer component to load the data into the pipeline as an in-memory artifact.

Question 10

A company has a TensorFlow model for image classification that must run on edge devices with limited memory. They need to reduce the model size without significant accuracy loss. Which technique should they use?

Accepted Answer

Post-training quantization using TensorFlow Lite.. Post-training quantization with TensorFlow Lite converts a trained model's weights from 32-bit floats to 8-bit integers, reducing model size by ~4x and speeding up inference on edge devices with minimal accuracy loss. TensorFlow Lite is purpose-built for edge deployment, making this the most direct and practical technique for the stated constraint.

Answer

Knowledge distillation to train a smaller student model.

Answer

Pruning the model weights to zero out unimportant connections.

Answer

Use a larger VM for training.

Question 11

You need to perform a large-scale feature computation on streaming data from Pub/Sub, transforming raw events into features, and writing results to Vertex AI Feature Store for online serving. Which Google Cloud architecture is most appropriate?

Accepted Answer

Use Dataflow streaming pipeline with Apache Beam to read from Pub/Sub, compute features, and write to Feature Store. Dataflow with Apache Beam is the purpose-built managed service for streaming ETL on Google Cloud, natively integrating with Pub/Sub as a source and Vertex AI Feature Store as a sink. Beam's windowing, watermarks, and exactly-once semantics handle the large-scale, continuous feature computation required for online serving. It provides autoscaling and managed infrastructure, which is exactly what a production streaming feature pipeline needs.

Answer

Use Dataproc with Spark Streaming to read from Pub/Sub and write to Feature Store

Answer

Use Cloud Functions triggered by Pub/Sub to compute features and update Feature Store

Answer

Use Cloud Run to consume Pub/Sub messages and update Feature Store via a service

Question 12

A company is using Vertex AI Vizier for hyperparameter tuning of a model with 5 integer hyperparameters, each with a range of 10-100. They have a budget of 50 trials and want to maximize the chance of finding the best configuration. Which Vizier algorithm should they use?

Accepted Answer

Bayesian optimization (GP bandit). Bayesian optimization (GP bandit) is Vizier's default and most sample-efficient algorithm, using a Gaussian Process surrogate model to balance exploration and exploitation across the 5-dimensional hyperparameter space. With only 50 trials over a large search space, it converges on promising regions far faster than uninformed methods. This makes it the best choice for maximizing the chance of finding the optimal configuration within a limited budget.

Answer

Grid search

Answer

Simulated annealing

Answer

Random search

Question 13

A company wants to implement a document processing solution that extracts key information from invoices and receipts. They have limited ML expertise and want to use a pre-trained solution as much as possible. Which Google Cloud service should they use?

Accepted Answer

Document AI with a pre-trained invoice processor.. Document AI with a pre-trained invoice processor is the correct choice because it provides a fully managed, pre-trained solution specifically designed for extracting structured data (e.g., vendor name, invoice number, line items) from invoices and receipts. This aligns with the company's limited ML expertise and desire to use a pre-trained solution, requiring no custom model training or complex coding.

Answer

AutoML Natural Language with custom entity extraction.

Answer

Vertex AI Workbench with custom Python scripts.

Answer

Cloud Vision API with OCR.

Question 14

An engineer deploys a model to a Vertex AI endpoint with minReplicas=1 and maxReplicas=3. The endpoint receives a sudden traffic spike, but it does not scale up beyond 1 replica. The CPU utilization target is 60%. What is the most likely cause?

Accepted Answer

The CPU utilization is below the target threshold, so the autoscaler does not add replicas.. Vertex AI's autoscaler uses CPU utilization as a metric to decide when to add replicas. If the CPU utilization remains below the 60% target threshold, the autoscaler will not trigger scale-up, even during a traffic spike. The endpoint is configured with minReplicas=1 and maxReplicas=3, but without exceeding the target, it stays at the minimum.

Answer

The model is not deployed correctly.

Answer

The endpoint is configured with the wrong machine type.

Answer

The endpoint is using GPU which cannot autoscale.

Question 15

A financial services company uses BigQuery ML to build a logistic regression model for fraud detection. The model is trained on the last 6 months of transaction data (about 50 million rows). After deployment, the fraud detection team notices a high false positive rate, causing customer dissatisfaction and extra manual review costs. The model is currently retrained monthly. The team wants to reduce false positives without sacrificing recall. They have access to real-time transaction streaming and can compute new features quickly. What is the most effective approach?

Accepted Answer

Add engineered features like rolling transaction count and velocity per user. Adding engineered features like rolling transaction count and velocity per user directly addresses the high false positive rate by providing the logistic regression model with more discriminative temporal signals. Since the team has access to real-time streaming and can compute features quickly, these features capture behavioral patterns that reduce false positives without sacrificing recall, and logistic regression can effectively leverage them with proper feature engineering.

Answer

Replace logistic regression with gradient boosted trees (XGBoost) in BigQuery ML

Answer

Use Vertex AI AutoML Tables to train a more complex model

Answer

Increase retraining frequency to daily

Question 16

A marketing agency uses Vertex AI AutoML Vision to classify social media images into brand logos and generic content. They have 5,000 images per class. The model achieves 95% accuracy on validation set, but in production it misclassifies many images that contain logos in unusual angles or lighting. They have limited ML expertise and want to improve robustness. Which action should they take?

Accepted Answer

Augment the training set with images that have varied angles and lighting.. The core issue is a domain shift between the training data (likely clean, canonical logo images) and production data (logos at unusual angles and lighting). Augmenting the training set with those specific variations directly addresses the lack of robustness by exposing the model to the missing edge cases during training, which is the most effective and simplest fix for a team with limited ML expertise using AutoML Vision.

Answer

Switch to a custom CNN model trained with data augmentation.

Answer

Deploy the model with a lower confidence threshold.

Answer

Use Vertex AI Matching Engine for similarity search instead.

Question 17

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built Google Cloud Pipeline Components (GCPC) to train a custom model. Which component should the engineer use to submit a custom training job to Vertex AI?

Accepted Answer

CustomJob. The CustomJob component is the correct choice because it is the pre-built GCPC component specifically designed to submit a custom training job to Vertex AI. It allows the engineer to specify a custom container image or a Python training script, along with machine configuration and hyperparameters, directly within a Vertex AI pipeline. Other components serve different purposes, such as hyperparameter tuning, batch predictions, or model deployment.

Answer

HyperparameterTuningJob

Answer

BatchPredictionJob

Answer

ModelDeploy

Question 18

Which TWO options are best practices for reducing model serving latency on Vertex AI Endpoints? (Choose two.)

Accepted Answer

Optimize the model using quantization or pruning. Option B is correct because quantization (e.g., reducing weights from FP32 to INT8) and pruning (removing redundant parameters) shrink the model size and reduce the compute required per inference, directly lowering prediction latency on Vertex AI Endpoints. Option C is correct because deploying the model in the same region as the clients minimizes network round-trip time, which is a significant component of end-to-end serving latency for online predictions. Option A is not a best practice for latency specifically: a larger machine with more memory may help with throughput or memory-bound models, but it does not inherently reduce per-request latency and increases cost. Option D is wrong because batch prediction is an asynchronous, offline mode that does not serve real-time requests and is not a latency optimization for online endpoints. Option E is not a supported Vertex AI Endpoint feature; there is no endpoint-level 'model caching' toggle that reduces serving latency.

Answer

Use a larger machine type with more memory

Answer

Use batch prediction instead of online prediction

Answer

Enable model caching at the endpoint

Question 19

A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?

Accepted Answer

Add more labeled images to the dataset. Adding more labeled images directly addresses the most common cause of low accuracy in AutoML vision models: insufficient or unrepresentative training data. Vertex AI AutoML relies on transfer learning from pre-trained models, and its performance is heavily dependent on the quality and quantity of labeled examples. Before adjusting hyperparameters or infrastructure, the first step should always be to improve the dataset, as AutoML is designed to handle model architecture and training budget automatically.

Answer

Switch to a custom model

Answer

Increase the training budget

Answer

Reduce image size to speed up training

Question 20

You are using DVC for data versioning in an ML project on Google Cloud. Your training data is stored in Cloud Storage. You want to track a new version of the dataset after preprocessing. Which DVC command should you use to register the changes?

Accepted Answer

dvc add data/processed. The `dvc add` command registers a file or directory with DVC, creating a .dvc metafile that captures the hash and metadata of the data, and adds the actual data to the DVC cache. After preprocessing produces a new dataset in data/processed, running `dvc add data/processed` creates the versioned pointer that DVC tracks in Git, which is exactly what 'registering the changes' means.

Answer

dvc push

Answer

dvc run -n preprocess

Answer

dvc commit