PMLE Practice Test 8 — 10 Questions

Question 1

A team wants to ensure that only approved models are deployed to production. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI Model Registry with versioning and alias.. Vertex AI Model Registry with versioning and alias (Option E) is the correct feature because it allows teams to manage model lifecycle, track approved versions, and assign aliases (e.g., 'champion' or 'production') to designate which model is approved for deployment. This ensures only vetted models are promoted to production, aligning with governance and compliance requirements.

Answer

Vertex AI Experiments.

Answer

Cloud DLP.

Answer

Vertex AI Pipelines.

Answer

Vertex AI Feature Store.

Question 2

An ML engineer is deploying a large BERT-based natural language processing model for real-time inference on Vertex AI Prediction. The model has a large memory footprint (2GB) and experiences unpredictable traffic spikes up to 10x the baseline. The engineer needs to minimize latency and cost while handling spiky traffic. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Use a custom prediction routine with model quantization to reduce model size.. Model quantization reduces the memory footprint of a BERT model (e.g., from 2GB to ~500MB with INT8 quantization), which directly lowers inference latency and cost by enabling faster loading and more efficient use of hardware. This is critical for real-time inference with unpredictable traffic spikes, as smaller models scale more easily and reduce the need for excessive replicas.

Answer

Configure the endpoint to use manual scaling with a fixed number of replicas equal to peak traffic.

Answer

Enable automatic scaling with a maximum of 3 replicas to limit cost.

Answer

Set up model monitoring to detect prediction drift and retrain regularly.

Question 3

A data scientist trains an XGBoost model on Vertex AI with a custom container. The model performs well on a held-out test set but fails to generalize in production. They suspect data leakage between training and validation. What is the best practice to prevent this?

Accepted Answer

Store and serve features using Vertex AI Feature Store with point-in-time correctness. Vertex AI Feature Store with point-in-time correctness ensures that for each training example, only feature values that were known at the time of the prediction (i.e., before the label occurred) are used. This prevents future data from leaking into the training set, which is the most common cause of poor generalization when temporal ordering matters. The Feature Store automatically retrieves the latest feature value as of a specified timestamp, eliminating the need for manual joins and windowing logic.

Answer

Implement feature engineering in Vertex AI Pipelines to ensure temporal ordering

Answer

Store all features in BigQuery and join on timestamp during training and serving

Answer

Use Vertex AI AutoML instead of custom training

Question 4

What is the primary purpose of Vertex AI Edge Manager?

Accepted Answer

To deploy and manage ML models on edge devices at scale. Vertex AI Edge Manager is specifically designed to deploy, monitor, and manage ML models on edge devices at scale. It handles model packaging, over-the-air updates, and health monitoring across fleets of edge devices, which is distinct from simply running batch predictions or converting model formats.

Answer

To run batch predictions on edge devices

Answer

To convert models to TensorFlow Lite automatically

Answer

To train models on edge devices using federated learning

Question 5

A team wants to implement automated model documentation that captures training data, feature importance, evaluation metrics, and intended use. Which Vertex AI feature supports this?

Accepted Answer

Vertex AI Model Registry with model cards. Model cards in Vertex AI Model Registry provide a standardised template for documenting model details.

Answer

Vertex AI Metadata

Answer

Vertex AI Explainable AI

Answer

Vertex AI Pipelines

Question 6

You need to preprocess a large dataset (terabytes) for training a TensorFlow model. The preprocessing includes scaling and bucketizing features, and the same transformations must be applied during serving. Which tool should you use?

Accepted Answer

Dataflow with Apache Beam and tf.Transform. TensorFlow Transform (tf.Transform) allows you to define preprocessing pipelines that are applied consistently during training and serving. It computes statistics over the full dataset and creates a TensorFlow graph for serving.

Answer

Dataproc with Spark ML

Answer

Vertex AI Feature Store

Answer

BigQuery ML

Question 7

A company trains models using Vertex AI Training and wants to share the resulting model artifacts with a different team in another Google Cloud project. What is the most secure way to grant access?

Accepted Answer

Create a new service account in the other project, then grant it the 'roles/storage.objectViewer' role on the bucket.. It follows the principle of least privilege and cross-project access best practices. By creating a dedicated service account in the target project and granting it the 'roles/storage.objectViewer' role on the specific Cloud Storage bucket, you avoid exposing the bucket publicly and avoid sharing the Vertex AI model resource directly, which would grant broader permissions than necessary. This approach ensures that only the service account can read the model artifacts, and the other team can use that service account to access the bucket securely.

Answer

Use BigQuery to copy the model artifacts and share the BigQuery dataset.

Answer

Share the Vertex AI model resource directly by adding the other project's members to the IAM policy on the model.

Answer

Set the Cloud Storage bucket containing the artifacts to 'public' access.

Question 8

Refer to the exhibit. An ML engineer in the team needs to deploy the model to an endpoint. The engineer is assigned the 'roles/aiplatform.user' role at the project level but still cannot deploy. What is the most likely reason?

Accepted Answer

The model's resource-level IAM policy only grants the 'roles/aiplatform.user' role, which does not include deploy permission.. The 'roles/aiplatform.user' role at the project level grants permissions to use AI Platform resources, but it does not include the 'aiplatform.models.deploy' permission required to deploy a model to an endpoint. Model deployment is controlled by resource-level IAM policies, and if the model's resource-level policy only grants 'roles/aiplatform.user', the deploy action is denied. The correct role for deployment is 'roles/aiplatform.admin' or a custom role with the deploy permission.

Answer

The service account 'sa-training' is using all the model's quota.

Answer

Alice does not have any IAM role on the project.

Answer

Alice needs to be granted the 'roles/aiplatform.admin' role at the project level.

Question 9

To enable collaboration on notebook-based experiments across teams, what is the recommended approach in Google Cloud?

Accepted Answer

Use Colab Enterprise notebooks with shared runtimes and IAM permissions. Colab Enterprise notebooks with shared runtimes and IAM permissions is the recommended approach because it provides a fully managed, collaborative environment where multiple users can work on the same notebook simultaneously, with fine-grained access control via IAM and consistent runtime configurations. This eliminates version conflicts and environment drift, which are common in distributed notebook workflows.

Answer

Share Docker images containing the notebook environment

Answer

Each team member works on their own local Jupyter notebook and shares screenshots

Answer

Store notebooks in a Cloud Storage bucket and open them with Vertex AI Workbench

Question 10

A data scientist trained a model on a single GPU but needs to train on multiple GPUs for a larger dataset. They observe that training time does not decrease linearly with additional GPUs. Which common issue is most likely?

Accepted Answer

Data pipeline bottleneck.. Data pipeline bottleneck. When training on multiple GPUs, the expected speedup is linear if data loading and preprocessing can keep up with the GPUs. However, if the data pipeline is slow (e.g., due to disk I/O, network latency, or insufficient CPU preprocessing), GPUs will spend time idle waiting for data, preventing linear scaling. Option A is incorrect because overfitting affects model accuracy, not training speed. Option B is incorrect because model architecture simplicity does not directly cause non-linear speedup; simpler architectures may actually speed up training. Option C is incorrect because learning rate affects convergence behavior, not the efficiency of parallel training.

Answer

Overfitting.

Answer

Model architecture too simple.

Answer

Learning rate too high.