PMLE Practice Test 29 — 15 Questions

Question 1

A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry (B) is the centralized repository for managing the lifecycle of ML models, providing a single source of truth for governance across an organization, which directly matches the requirement for a centralized model registry. Model versioning and aliases (D) are core capabilities of the Model Registry that let teams track multiple model versions, assign meaningful aliases (e.g., 'production', 'staging'), and control which version is deployed, which is essential for governance and reproducibility. Together, B and D provide the registry plus the version/alias controls needed for centralized model governance. Vertex AI Feature Store (A) manages feature storage and serving, not model registration, so it does not fulfill the model registry requirement. Vertex AI Experiments (C) tracks experiment runs, parameters, and metrics for training, not model governance or registration. Vertex AI Metadata (E) stores metadata about artifacts and executions in a lineage graph, but it is not the centralized model registry itself.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Vertex AI Metadata

Question 2

A developer needs to transcribe phone calls with high accuracy for a call center analytics application. The audio is in English and has background noise. Which Speech-to-Text model should they choose?

Accepted Answer

telephony. The 'telephony' model is specifically optimized for transcribing audio from phone calls, which often includes background noise, low fidelity, and narrowband audio. It is designed to handle the unique characteristics of telephony audio, such as 8kHz sampling rate and compression artifacts, providing higher accuracy for call center analytics. Other models like 'latest_short' and 'latest_long' are general-purpose and may not perform as well on noisy phone calls.

Answer

latest_short

Answer

latest_long

Answer

Any model; they are all equivalent

Question 3

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Accepted Answer

Initialize the Vertex AI SDK, then create an experiment, then log metrics and parameters, then record artifacts, then review lineage.. Start with SDK setup, then create an experiment, log metrics, record artifacts, and review lineage.

Answer

Create an experiment, then initialize the SDK, then log metrics, then review lineage, then record artifacts.

Answer

Initialize the SDK, then log metrics and parameters, then create an experiment, then record artifacts, then review lineage.

Answer

Initialize the SDK, then create an experiment, then record artifacts, then log metrics, then review lineage.

Question 4

Which TWO of the following are low-code machine learning solutions on Google Cloud?

Accepted Answer

BigQuery ML. BigQuery ML (D) is a low-code ML solution because it allows users to create, train, and deploy machine learning models using standard SQL queries directly within BigQuery, eliminating the need for custom coding in Python or other programming languages. Vertex AI AutoML (E) is also low-code as it provides a graphical interface and automated pipeline to train high-quality models with minimal manual intervention, handling feature engineering, model selection, and hyperparameter tuning automatically.

Answer

TensorFlow

Answer

scikit-learn

Answer

PyTorch

Question 5

A data scientist wants to use a pre-trained ResNet model from Keras Applications and fine-tune it on a small custom dataset. Which approach should they take to avoid overfitting?

Accepted Answer

Freeze the first few layers and train the rest.. Freezing the earlier layers (which capture general features) and only training the later layers is a common transfer learning approach for small datasets, reducing overfitting.

Answer

Add more convolutional layers to the model.

Answer

Use a larger learning rate to speed up training.

Answer

Train the entire model from scratch on the custom dataset.

Question 6

You are deploying a model to a Vertex AI endpoint and need to minimize latency for online predictions. Which machine type should you choose?

Accepted Answer

n1-standard-2 with NVIDIA Tesla T4. For online predictions with minimal latency, a machine type with a GPU is essential because it accelerates model inference. The n1-standard-2 with NVIDIA Tesla T4 provides GPU acceleration, which significantly reduces latency compared to CPU-only instances. The other options lack GPUs and are not optimized for low-latency inference.

Answer

e2-standard-2

Answer

n1-standard-2

Answer

n1-highmem-2

Question 7

A company is building a document processing pipeline using Document AI to extract data from invoices. They want to ensure high accuracy and handle edge cases where the model may be uncertain. Which THREE steps should they include in their pipeline?

Accepted Answer

Regularly retrain the processor using human-verified data. Option A is correct because regularly retraining the processor with human-verified data continuously improves extraction accuracy and adapts the model to new invoice variations and edge cases over time. Option D is correct because enabling Human-in-the-Loop (HITL) routes documents with low confidence scores to human reviewers, ensuring uncertain or edge-case extractions are validated and corrected before entering downstream systems. Option E is correct because a custom processor trained on the company's specific invoice format captures their unique layouts, fields, and terminology, yielding higher accuracy than a generic model. Option B is not appropriate because using the pre-built invoice parser without modification offers no tuning for the company's specific formats and provides no mechanism for handling uncertainty. Option C is not appropriate because AutoML Vision is an image classification service, not a document entity-extraction tool, and classifying invoice types does not extract the required field data or address low-confidence edge cases.

Answer

Use the pre-built invoice parser without any modifications

Answer

Use AutoML Vision to classify invoice types

Question 8

Drag and drop the steps to set up a feature store for ML features using Vertex AI Feature Store in the correct order.

Accepted Answer

Define entity type and features, then ingest data, then serve, then monitor.. The correct sequence to set up a feature store using Vertex AI Feature Store is: first define the entity type and its features (schema), then ingest feature data into the store, then serve those features to models for predictions, and finally monitor the store for data quality, freshness, and drift. This ensures a logical flow from definition to consumption to observability.

Answer

Ingest data, then define entity type and features, then serve, then monitor.

Answer

Define entity type and features, then serve, then ingest data, then monitor.

Answer

Monitor, then define entity type and features, then ingest data, then serve.

Question 9

A user receives the error "Deployment failed due to insufficient memory. Please use a machine type with higher memory." when deploying an AutoML model. What should they do?

Accepted Answer

Use machine type n1-highmem-2. When deploying an AutoML model, memory constraints are a common cause of deployment failures. Using a machine type with higher memory, such as n1-highmem-2, helps ensure the model can be loaded and served without out-of-memory errors. The other options do not address memory requirements: changing the region does not affect compute resources, increasing the min replica count does not increase per-instance memory, and removing traffic-split flags does not resolve memory issues.

Answer

Change the region to us-west1

Answer

Increase the min-replica-count to 2

Answer

Remove the traffic-split flag

Question 10

You are fine-tuning a large language model (LLM) from Hugging Face Transformers using Vertex AI Training. The model has 7 billion parameters and does not fit into the memory of a single GPU. You need to train across multiple GPUs, splitting the model layers across devices. Which distributed training approach should you use?

Accepted Answer

Model parallelism using pipeline parallelism. When a model is too large to fit on a single GPU, model parallelism is required to split the model's layers across multiple devices. Pipeline parallelism is a specific form of model parallelism that partitions layers into stages and pipelines micro-batches across devices, enabling training of models like a 7B-parameter LLM across multiple GPUs. This is the correct approach when memory, not throughput, is the binding constraint.

Answer

Data parallelism with MultiWorkerMirroredStrategy

Answer

Mixed precision training (FP16)

Answer

Data parallelism with tf.distribute.MirroredStrategy

Question 11

A team wants to implement CI/CD for their ML models using Cloud Build. They have a pipeline that trains a model and deploys it. What is the best practice for triggering the pipeline when a new commit is pushed to the source repository?

Accepted Answer

Configure a Cloud Build trigger on the source repository (e.g., Cloud Source Repositories, GitHub). Cloud Build natively supports triggers that automatically start a pipeline when a new commit is pushed to a connected source repository (e.g., Cloud Source Repositories, GitHub, Bitbucket). This is the simplest, most event-driven approach, requiring no polling, custom services, or additional messaging infrastructure. It directly maps the git push event to a build invocation, ensuring near-instantaneous pipeline execution.

Answer

Set up a Cloud Scheduler job to poll the repository periodically

Answer

Deploy a custom web service on App Engine to call Cloud Build API

Answer

Use Pub/Sub to notify Cloud Build of new commits

Question 12

A machine learning engineer is exporting a trained model from Vertex AI Training to the Model Registry. Which artifact should they upload as the model artifact?

Accepted Answer

The saved model directory containing the model file(s) and any custom dependencies.. When exporting a trained model from Vertex AI Training to the Model Registry, the correct artifact is the saved model directory that contains the model file(s) (e.g., SavedModel format for TensorFlow, model.pkl for scikit-learn) along with any custom dependencies required for serving. This ensures the model can be deployed consistently to endpoints or batch predictions, as the Model Registry expects a self-contained artifact that includes both the model binary and its runtime dependencies.

Answer

Only the model checkpoint file (.ckpt or .h5).

Answer

The entire training directory including training code and logs.

Answer

A zip file of the training source code.

Question 13

You need to serve multiple models on a single Vertex AI endpoint to reduce costs. How can you achieve this?

Accepted Answer

Use Vertex AI Prediction with multi-model serving by deploying multiple models to one endpoint with traffic splits.. Vertex AI Prediction supports multi-model serving, allowing you to deploy multiple models to a single endpoint and use traffic splits to route a percentage of requests to each model. This reduces costs by sharing underlying infrastructure (e.g., compute resources) across models, rather than provisioning separate endpoints or containers for each model.

Answer

Use Cloud Run to serve each model separately.

Answer

Package all models into a single container and deploy that container.

Answer

Deploy each model to its own endpoint and use a load balancer.

Question 14

A company wants to train a custom machine learning model on Vertex AI using a pre-built container for scikit-learn. They want to use spot VMs to reduce costs. However, the training job fails intermittently due to preemption. Which TWO actions should they take to ensure the training job completes successfully?

Accepted Answer

Set the max_retry_count in the worker pool spec to a value greater than 0. To handle spot VM preemptions, the training job must be restartable. Using checkpoints allows the job to resume from the last saved state. Vertex AI automatically retries on preemption if the job is restartable (managed by the service). Setting max_retry_count in the worker pool spec allows Vertex AI to automatically restart the job after preemption. Also, reducing machine type or increasing parallel trials are not direct solutions.

Answer

Use a larger machine type to reduce training time

Answer

Increase the number of parallel trials in hyperparameter tuning

Answer

Set the worker_pool_specs to use spot VMs by setting spot=True

Question 15

You need to serve a large embedding model for similarity search with low latency. The model was trained to generate 256-dimensional embeddings. You plan to use Vertex AI Vector Search. Which index type should you choose to balance accuracy and performance for a dataset with 10 million vectors?

Accepted Answer

Approximate nearest neighbor (ANN) index using ScaNN. Vertex AI Vector Search uses ScaNN (Scalable Nearest Neighbors) as its underlying ANN algorithm, which is specifically designed for high-dimensional embeddings (like 256-d) and large-scale datasets (10M vectors). ScaNN balances accuracy and performance by employing anisotropic quantization and tree-based partitioning, making it the optimal choice for low-latency similarity search without requiring exhaustive comparison.

Answer

Tree-based index

Answer

Brute-force index

Answer

Hash-based index