PMLE Practice Test 25 — 15 Questions

Question 1

A pipeline includes a component that produces a model artifact. The team wants to automatically detect skew between the training data distribution and the serving data distribution. Which three best practices should they implement? (Choose three.)

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Compare statistics using a dedicated component and alert on threshold exceedance. Option A is correct because skew detection requires a dedicated comparison component that evaluates training versus serving statistics and raises an alert when a configured threshold (e.g., a divergence metric like KL divergence or PSI) is exceeded. Option C is correct because you must compute statistics on the serving data itself, typically via a component that ingests the live inference data and emits a statistics artifact for comparison. Option E is correct because the training data statistics should be persisted and passed as a Dataset artifact so the comparison component can consume them reproducibly across pipeline runs. Option B is wrong because in-memory data passing does not persist artifacts and is unsuitable for cross-component, cross-run statistical comparison. Option D is wrong because disabling caching does not improve statistical correctness and only adds unnecessary recomputation cost.

Answer

Use in-memory data passing for efficiency

Answer

Disable caching to ensure fresh statistics

Question 2

A company is building a document processing pipeline for invoices. They need to extract key fields (invoice number, date, total amount) and allow human review for invoices over $10,000. Which TWO Google Cloud services/features should they combine?

Accepted Answer

Human-in-the-Loop (HITL) on Document AI. Option D is correct because Document AI's specialized Invoice parser processor is purpose-built to extract structured fields such as invoice number, invoice date, and total amount from invoice documents, which is exactly the extraction requirement in this pipeline. Option B is correct because Document AI's Human-in-the-Loop (HITL) feature lets you define confidence thresholds and route documents for manual review, which directly satisfies the requirement to have humans review invoices over $10,000 before downstream processing. Together, the Invoice parser handles automated field extraction while HITL provides the human verification step for high-value invoices. Option A is not the best fit because Cloud Vision API provides generic OCR without invoice-specific field extraction or built-in human review workflows. Option C is incorrect because AutoML Tables is a tabular ML service for structured data prediction, not for predicting missing fields in parsed documents. Option E is incorrect because Cloud Translation API only translates text and does not extract invoice fields or support human review.

Answer

Cloud Vision API for OCR

Answer

AutoML Tables to predict missing fields

Answer

Cloud Translation API to translate invoices

Question 3

Which TWO strategies help ensure data consistency when multiple teams are contributing features to a shared Vertex AI Feature Store?

Accepted Answer

Define and enforce feature schemas using the Feature Store API.. Defining and enforcing feature schemas using the Vertex AI Feature Store API ensures that all teams adhere to a consistent data structure (e.g., fixed feature names, data types, and value ranges). This prevents schema drift and ingestion conflicts, which are common when multiple teams independently push features to the same feature store. Without schema enforcement, one team might inadvertently change a feature's data type or add unexpected values, breaking downstream models.

Answer

Each team should create their own feature store to avoid conflicts.

Answer

Use only batch ingestion to keep features synchronized.

Answer

Allow each team to independently define feature engineering logic.

Question 4

A company wants to use Vertex AI Vizier to tune hyperparameters for a PyTorch model. They have a limited budget of 50 training jobs. The objective metric is validation accuracy, and they want to find the best configuration efficiently. Which algorithm should they choose?

Accepted Answer

Bayesian optimization using Vertex AI Vizier.. Vertex AI Vizier is a hyperparameter tuning service that uses Bayesian optimization to efficiently search the hyperparameter space. With a limited budget of 50 trials, Bayesian optimization is more sample-efficient than random or grid search, making it the best choice.

Answer

Random search with 50 random configurations.

Answer

Use a custom algorithm implemented in the training code.

Answer

Grid search with 50 evenly spaced points.

Question 5

An organisation wants to use Document AI to process contracts but requires human review for high-risk clauses. Which feature should they enable?

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) is the correct feature because it allows Document AI to automatically process contracts while routing high-risk clauses to human reviewers for validation. This balances automation efficiency with the need for expert oversight on sensitive content, which is a core requirement for compliance-driven document processing.

Answer

Batch Processing

Answer

Online Prediction

Answer

AutoML Training

Question 6

You are deploying a PyTorch model for online predictions on Vertex AI. The model expects input tensors and performs GPU-accelerated inference. You want to minimize prediction latency and maximize throughput. Which approach should you use?

Accepted Answer

Deploy using a prebuilt PyTorch serving container with NVIDIA Triton Inference Server.. NVIDIA Triton Inference Server provides advanced features like dynamic batching, concurrent model execution, and GPU scheduling that maximize throughput and minimize latency for GPU-accelerated inference. Vertex AI's prebuilt PyTorch serving container with Triton is specifically designed to handle online prediction workloads efficiently, outperforming a plain custom container without an inference server.

Answer

Package the model in a custom container without any inference server.

Answer

Use Vertex AI Model Optimization to quantize the model to FP16 and deploy using the optimized model.

Answer

Use batch prediction instead of online prediction to reduce latency.

Question 7

A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?

Accepted Answer

Use a centralized feature store in a shared project and grant access to other projects via IAM. Vertex AI Feature Store is project-scoped, so to share features across projects, the recommended pattern is to create a centralized feature store in a shared (host) project and grant IAM roles to users/service accounts in other projects. This avoids duplication and ensures a single source of truth for feature definitions and online serving.

Answer

Export features to BigQuery datasets in each project

Answer

Use Vertex AI Feature Store's feature view for cross-project access

Answer

Create separate feature stores in each project and synchronize them with Dataflow

Question 8

A company is deploying a model on Vertex AI for online predictions with strict latency SLOs. The model requires GPU acceleration. Which TWO configurations should they consider to meet the SLOs while optimizing cost?

Accepted Answer

Set min_replica_count to handle base traffic and max_replica_count to handle spikes. Option B is correct because configuring min_replica_count to cover baseline traffic keeps enough warm replicas to serve steady requests within latency SLOs, while max_replica_count allows Vertex AI autoscaling to absorb traffic spikes without over-provisioning permanently. Option C is correct because the model requires GPU acceleration, and GPU-enabled machine types such as n1-standard-4 with an NVIDIA T4 provide the necessary hardware acceleration for online predictions. Option A is incorrect because n1-highmem-32 without a GPU cannot satisfy the GPU acceleration requirement. Option D is incorrect because setting min_replica_count=0 can cause cold-start delays and scale-from-zero latency that violate strict latency SLOs. Option E is incorrect because disabling autoscaling and fixing replicas at peak load wastes cost and does not optimize for variable traffic.

Answer

Use n1-highmem-32 machine types without GPU

Answer

Enable autoscaling with min_replica_count=0 and max_replica_count=10

Answer

Disable autoscaling and set a fixed number of replicas equal to peak load

Question 9

An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store is purpose-built to centrally define, store, and serve ML features for both training (batch) and online inference (low-latency) with a consistent feature definition, which directly eliminates training-serving skew. It provides a single source of truth so teams across projects can share features without manual coordination.

Answer

Cloud Data Catalog

Answer

Vertex AI Model Registry

Answer

Cloud Storage with versioning

Question 10

A team deploys a PyTorch model on Vertex AI for online predictions. They notice that after deployment, the latency increases over time, especially during peak hours. The model is served using a custom container. What is the most likely cause?

Accepted Answer

The prediction requests are not being batched, and the model inference code is not optimized for concurrency.. The latency increase over time, especially during peak hours, indicates that the model inference code is not handling concurrent requests efficiently. Without batching or optimized concurrency, each request is processed sequentially, causing a queue buildup under load. This is a common issue with custom containers on Vertex AI when the prediction handler is single-threaded or lacks async processing.

Answer

The custom container does not have a health check, causing instances to be prematurely terminated.

Answer

The model is not using GPU even though a GPU machine is selected.

Answer

The model is too large for the machine's memory, causing swapping.

Question 11

A machine learning engineer is deploying a TensorFlow model on an edge device with limited memory and compute. The model needs to perform inference with low latency. The engineer has a trained float32 model. Which model compression technique should be applied first to reduce the model size and improve inference speed without significant accuracy loss?

Accepted Answer

Post-training quantization to INT8. Post-training quantization to INT8 is the correct first step because it directly reduces the model size by approximately 4x (from 32-bit floats to 8-bit integers) and speeds up inference on edge devices by leveraging integer-optimized hardware (e.g., ARM NEON or Qualcomm Hexagon). This technique requires no retraining and typically yields minimal accuracy loss for most TensorFlow models, making it the fastest path to deploy on resource-constrained devices.

Answer

Knowledge distillation

Answer

Quantization-aware training

Answer

Weight pruning

Question 12

A company uses Vertex AI Pipelines with prebuilt components for data processing, training, and deployment. They need to integrate a custom validation step written in Python. What is the correct way to include this as a component?

Accepted Answer

Create a custom component using the Vertex AI Pipelines SDK @component decorator. The Vertex AI Pipelines SDK provides a `@component` decorator that allows you to define a custom Python function as a pipeline component. This decorator automatically handles packaging the Python code into a container image, generating the component specification, and integrating it seamlessly with the pipeline orchestration engine. It is the idiomatic and recommended way to add custom validation logic without manually managing Docker or infrastructure.

Answer

Package the code in a Docker container and reference it as a custom job

Answer

Define the step in the YAML pipeline definition using arbitrary Python commands

Answer

Use a Cloud Function as a pipeline step

Answer

Write a standalone Python script and call it using a Cloud Shell step

Question 13

A data scientist has trained a scikit-learn model locally and wants to deploy it to Vertex AI for online predictions with low latency. The model is a small RandomForestClassifier (100 MB). What is the recommended way to deploy this model?

Accepted Answer

Upload the model to Vertex AI Model Registry using the pre-built scikit-learn serving container.. Vertex AI provides a pre-built container for scikit-learn that is optimized for serving predictions with low latency. For a small RandomForestClassifier (100 MB), this container handles model loading, request routing, and scaling automatically, eliminating the need for custom infrastructure. This is the recommended approach for deploying scikit-learn models to Vertex AI for online predictions.

Answer

Deploy the model on a Kubernetes cluster with Istio.

Answer

Package the model as a Docker container with a custom prediction routine.

Answer

Export the model as a TensorFlow SavedModel and use the pre-built TF serving container.

Question 14

A company wants to cache predictions for identical requests to reduce latency and cost. They use Vertex AI Prediction with a custom container. Which GCP service should they use to implement prediction caching?

Accepted Answer

Cloud Memorystore for Redis. Cloud Memorystore for Redis is an in-memory data store with sub-millisecond latency, making it the ideal GCP service for caching prediction results keyed by request hash. It supports TTL-based expiration and high-throughput reads, which directly reduce latency and repeated model inference costs. This is the canonical GCP caching layer for Vertex AI prediction workloads.

Answer

Cloud Bigtable

Answer

Cloud Storage

Answer

Cloud Firestore

Question 15

Which THREE actions should be taken to manage model versions effectively?

Accepted Answer

Use Vertex AI Model Registry. Vertex AI Model Registry is a centralized repository that tracks, versions, and manages ML models. It enables you to organize models, assign aliases (like 'champion' or 'experiment'), and control deployment, ensuring reproducibility and governance across the model lifecycle.

Answer

Delete old versions immediately

Answer

Use the same model name for all versions