PMLE Practice Test 24 — 15 Questions

Question 1

A financial institution uses a machine learning model to approve loans. They must monitor for fairness and bias. Which THREE Google Cloud tools or features can help them achieve this? (Choose 3.)

Accepted Answer

What-If Tool. The What-If Tool (A) is a visualization toolkit integrated with Vertex AI that lets analysts probe a trained model with counterfactual and hypothetical inputs to inspect how predictions change across sensitive attributes like gender or ethnicity, directly supporting fairness and bias analysis. Vertex AI Model Monitoring (B) continuously tracks deployed models for training-serving skew and prediction drift, and can be configured with fairness-related metrics so the institution detects when a model's behavior degrades or becomes biased in production. Explainable AI (E) provides feature attributions (e.g., Sampled Shapley and integrated gradients) through Vertex AI, showing which features drive each loan decision so reviewers can confirm that protected attributes are not improperly influencing approvals. Cloud Data Loss Prevention (C) is for discovering and redacting sensitive data such as PII, not for evaluating model fairness, and Cloud Healthcare API (D) is a healthcare-specific data interoperability service unrelated to loan-model bias monitoring.

Answer

Cloud Data Loss Prevention

Answer

Cloud Healthcare API

Question 2

A company uses BigQuery to store feature data for ML training. A data engineer notices that a Vertex AI Training job is failing with 'Access Denied' errors when reading from a BigQuery table. The training job uses a custom service account that has been granted the 'bigquery.dataViewer' role on the dataset. What is the most likely cause of the failure?

Accepted Answer

The service account lacks the 'bigquery.jobs.create' permission in the project.. The 'bigquery.dataViewer' role grants permissions to read BigQuery data (e.g., bigquery.tables.getData), but it does not include the 'bigquery.jobs.create' permission. When a Vertex AI training job reads from BigQuery, it must first create a BigQuery job (a query job) to retrieve the data. Without 'bigquery.jobs.create' at the project level, the service account cannot initiate the read operation, resulting in an 'Access Denied' error even though it has data-level access.

Answer

The service account is not in the same project as the BigQuery dataset.

Answer

The BigQuery table is partitioned and requires row-level access.

Answer

The training job does not have the required network access to BigQuery.

Question 3

A team is responsible for monitoring the health of a Vertex AI pipeline that runs daily. Which THREE resources should they use to gain visibility into pipeline performance and failures? (Choose 3.)

Accepted Answer

Vertex AI Experiments for comparing pipeline runs. Vertex AI Experiments (C) is correct because it records and compares pipeline runs, letting the team track parameters, metrics, and artifacts across daily executions to evaluate performance trends. Cloud Monitoring (D) is correct because it collects pipeline run metrics and supports alerting policies so the team can be notified of failures or anomalies in the daily pipeline. Cloud Logging (E) is correct because pipeline step logs are written to Cloud Logging, providing detailed diagnostic visibility into individual step failures. Cloud Trace (A) is not the right fit here since it targets distributed request latency tracing rather than Vertex AI pipeline run health, and Cloud Composer (B) is a separate managed Airflow service for orchestrating DAGs, not the native monitoring surface for a Vertex AI pipeline.

Answer

Cloud Trace for analyzing distributed execution

Answer

Cloud Composer for tracking DAGs

Question 4

A financial company is building a fraud detection model. The dataset has 1% fraud cases and 99% legitimate transactions. Which technique should they use to handle the class imbalance?

Accepted Answer

Use class weighting or synthetic oversampling (SMOTE) during training. For highly imbalanced datasets like fraud detection (1% fraud), class weighting or synthetic oversampling (SMOTE) during training helps the model learn the minority class patterns without discarding valuable majority-class data. Class weighting adjusts the loss function to penalize minority-class misclassifications more heavily, while SMOTE generates synthetic minority samples to balance the class distribution, improving recall and F1 for the fraud class.

Answer

Randomly undersample the majority class to balance the dataset

Answer

Collect more data until the fraud rate increases

Answer

Train without any modifications; the model will naturally handle it

Question 5

You are using TensorFlow Transform (tf.Transform) to preprocess data for a model that will be deployed on Vertex AI. What is the primary benefit of using tf.Transform over Dataflow alone?

Accepted Answer

Training/serving skew prevention through a consistent transformation graph. tf.Transform computes statistics (e.g., min, max) on the full dataset, then generates a TensorFlow graph that applies the same transformation consistently at training and serving time. Dataflow alone does not ensure this consistency.

Answer

Support for GPUs during preprocessing

Answer

Built-in feature store integration

Answer

Faster data processing

Question 6

An ML engineer needs to monitor the online prediction latency of a Vertex AI Endpoint. Which metrics should they look at in Cloud Monitoring?

Accepted Answer

p50, p95, p99 latency. To monitor online prediction latency on a Vertex AI Endpoint, the engineer should look at percentile latency metrics such as p50, p95, and p99. These metrics provide a distribution of latency, showing typical and tail latencies, which are crucial for understanding user experience and identifying outliers.

Answer

Request count and error rate

Answer

Skew and drift scores

Answer

CPU/GPU utilization

Question 7

A large organization uses a multi-project setup with a central data lake. Different teams manage their own models. To enable cross-team sharing of features, they want to use Vertex AI Feature Store. What is the best practice to manage access?

Accepted Answer

Create a single Feature Store in a central project and grant fine-grained IAM roles. Creating a single Feature Store in a central project with fine-grained IAM roles is the best practice because it centralizes feature management while allowing cross-team access control at the feature group or feature level. Vertex AI Feature Store supports IAM roles like `aiplatform.featureStoreAdmin` and `aiplatform.featureStoreDataViewer` to grant granular permissions, enabling teams to share features without duplicating data or exposing sensitive information. This approach avoids data silos and ensures consistent governance across the organization.

Answer

Export features to Cloud Storage

Answer

Create separate Feature Stores per team project

Answer

Use BigQuery authorized views

Question 8

A marketing team wants to analyze customer reviews for sentiment without writing code. Which Google Cloud service should they use?

Accepted Answer

Cloud Natural Language API. The Cloud Natural Language API (option D) is the correct choice because it provides pre-trained models for sentiment analysis, entity recognition, and syntax analysis via a simple REST API, requiring no code beyond sending HTTP requests. This aligns perfectly with the requirement to analyze customer reviews for sentiment without writing code, as the API abstracts all ML complexity.

Answer

Cloud Dataflow

Answer

Vertex AI Workbench

Answer

BigQuery ML

Question 9

Your team has deployed a text classification model on Vertex AI Endpoints. You notice that the model's latency has increased significantly over the last week, but the request rate has remained stable. Which of the following is the most likely cause?

Accepted Answer

A change in the preprocessing logic that now includes a computationally expensive step. A computationally expensive preprocessing step directly increases per-request latency on the inference path, even when request rate is stable. Vertex AI Endpoints execute user-provided preprocessing code before model inference, so adding a heavy operation (e.g., large regex, image resizing, or external API call) will linearly increase response time for every prediction.

Answer

A sudden increase in the number of prediction requests

Answer

The model was replaced with a larger version without updating the endpoint

Answer

A misconfiguration in the autoscaling policy

Question 10

A company wants to implement a central model governance strategy using Vertex AI. They need to track model lineage, store evaluation metrics, and manage model versions across teams. Which THREE Vertex AI services should they use? (Choose 3)

Accepted Answer

Vertex AI Metadata. Vertex AI Metadata (A) is correct because it provides the managed ML metadata store that automatically tracks artifacts, executions, and contexts, enabling model lineage tracking across teams. Vertex AI Model Registry (B) is correct because it is the central repository for managing model versions, including versioning, aliasing, and deployment tracking across teams. Vertex AI Experiments (D) is correct because it records and compares experiment runs, storing evaluation metrics and parameters so teams can track model performance over time. Vertex AI Workbench (C) is not correct because it is an interactive notebook development environment, not a governance or lineage tracking service. Vertex AI Feature Store (E) is not correct because it manages feature storage and serving for training and prediction, not model lineage, metrics, or version management.

Answer

Vertex AI Workbench

Answer

Vertex AI Feature Store

Question 11

An ML engineer wants to monitor the performance of a Vertex AI Endpoint. Which TWO metrics are available in Cloud Monitoring for Vertex AI Endpoints? (Choose 2)

Accepted Answer

Error count. Vertex AI Endpoints automatically publish request-level metrics to Cloud Monitoring, including aiplatform.googleapis.com/endpoint/error_count, which tracks failed prediction requests and is essential for detecting serving problems. Option E (Prediction latency (p50, p95, p99)) is correct because Vertex AI Endpoints expose latency metrics such as aiplatform.googleapis.com/endpoint/prediction_latencies, which Cloud Monitoring reports as percentiles (p50, p95, p99) to characterize response-time distribution. Option A (Model accuracy) is not a built-in Cloud Monitoring metric for Endpoints; accuracy must be computed separately, for example via Vertex AI Model Monitoring or custom evaluation jobs. Option C (Feature skew score) belongs to Vertex AI Model Monitoring's skew/drift detection outputs, not to the standard Endpoint metrics in Cloud Monitoring. Option D (SHAP values) are explainability artifacts produced by Vertex Explainable AI, not time-series metrics available for an Endpoint in Cloud Monitoring.

Answer

Model accuracy

Answer

Feature skew score

Answer

SHAP values

Question 12

Which THREE are key capabilities of Vertex AI Feature Store?

Accepted Answer

Feature monitoring and validation to detect skew. Option B is correct because Vertex AI Feature Store provides feature monitoring and validation capabilities that detect training-serving skew and drift by comparing statistics of ingested feature values against baselines. Option C is correct because Feature Store offers online serving, exposing features through a low-latency endpoint so models can retrieve the latest feature values at prediction time. Option E is correct because Feature Store supports offline batch serving, allowing point-in-time-correct feature retrieval for training datasets via BigQuery or batch export. Option A is not a core capability of Feature Store; embeddings are generated by models or Vertex AI Embeddings APIs, not by the feature store itself. Option D is not a built-in capability; Feature Store ingests data through its API or BigQuery, and Kafka would require a custom pipeline rather than native streaming ingestion.

Answer

Automatic generation of feature embeddings

Answer

Real-time streaming ingestion from Apache Kafka

Question 13

An ML pipeline runs on Vertex AI and includes a component that uses a third-party library not available in the default Python environment. The team wants to avoid building a custom container image. Which approach should they use?

Accepted Answer

Use the packages_to_install parameter in @dsl.component. The `packages_to_install` parameter in the `@dsl.component` decorator allows you to specify a list of third-party Python packages (e.g., via pip) that will be installed at runtime in the component's execution environment, without needing to build a custom container image. This is the recommended approach in Vertex AI Pipelines when you need to use a library not present in the default Python environment, as it avoids the overhead of custom container creation while ensuring the dependency is available for that specific component.

Answer

Install the library using pip in the pipeline definition

Answer

Use a container component with a pre-built image

Answer

Add the library to the Vertex AI custom training image

Question 14

A retail company wants to implement a recommendation system using Recommendations AI. They need to generate personalized recommendations for users based on their browsing history and purchase behavior. Which THREE recommendation types are available in Recommendations AI?

Accepted Answer

recommended-for-you. Recommendations AI offers several built-in recommendation types, and recommended-for-you (B) is correct because it produces personalized product suggestions for a user based on that user's own browsing history, purchase behavior, and other interaction events. others-you-may-like (C) is also correct because it generates personalized recommendations related to a specific item the user is currently viewing, which fits the retail scenario of tailoring suggestions from user behavior. frequently-bought-together (D) is correct because it recommends complementary products commonly purchased alongside a given item, a standard Recommendations AI type for cross-sell use cases. The unmarked options do not belong: trending-now (A) and most-popular (E) are not valid Recommendations AI recommendation type identifiers, as the platform's types are named like recommended-for-you, others-you-may-like, frequently-bought-together, and similar-item, rather than those generic popularity labels.

Answer

trending-now

Answer

most-popular

Question 15

You need to run batch predictions on a large dataset stored in BigQuery using a Vertex AI model. The dataset contains 10 million rows, and each prediction takes about 100ms. You want to minimize cost and execution time. What should you do?

Accepted Answer

Use Vertex AI batch prediction with BigQuery as the source and sink.. Vertex AI batch prediction natively supports BigQuery as both input and output, eliminating the need for data export or custom pipelines. For 10 million rows at 100ms each, batch prediction processes them in parallel across multiple machines, minimizing execution time while avoiding the per-node costs of online prediction or the overhead of managing Dataflow or GKE clusters.

Answer

Export the BigQuery data to CSV in GCS, then run a custom Dataflow pipeline to make predictions.

Answer

Use Vertex AI online prediction and send all rows as separate requests.

Answer

Use a custom container running on Google Kubernetes Engine to perform inference.