PMLE Practice Test 6 — 10 Questions

Question 1

Match each Google Cloud AI/ML service to its primary purpose.

Accepted Answer

Vertex AI: End-to-end ML platform for building, training, and deploying models.. In this matching question, the correct pairings are: Option A (Vertex AI: End-to-end ML platform) is correct because Vertex AI unifies the ML workflow. Option C (Cloud AutoML: Train custom ML models with minimal coding) is correct because AutoML provides no-code training. Option E (Vision API: Analyze images for objects, faces, text) is correct because Vision API specializes in image analysis. Option B is wrong because Vertex AI is not primarily for graphical no-code training—that is AutoML's strength. Option D is wrong because Cloud AutoML is not an end-to-end platform—that's Vertex AI. Option F is wrong because Vision API does not analyze text; that is the Natural Language API's purpose.

Answer

Vertex AI: Train custom ML models using a graphical interface with minimal coding.

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Cloud AutoML: End-to-end ML platform for building, training, and deploying models.

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Vision API: Analyze text for sentiment, entity recognition, and syntax.

Question 2

Match each ML model interpretability method to its description.

Accepted Answer

LIME: Local interpretable model-agnostic explanations by approximating locally with a simpler model.. LIME (Local Interpretable Model-agnostic Explanations) provides local explanations by approximating the model's behavior near a specific prediction using a simpler interpretable model. SHAP (SHapley Additive exPlanations) uses Shapley values from game theory to fairly distribute feature contributions for individual predictions. Partial Dependence Plots (PDP) show the average marginal effect of one or two features on the predicted outcome across the dataset, making it a global method. Permutation Feature Importance measures the increase in prediction error when a feature's values are randomly shuffled, indicating feature importance globally. Common mistakes: confusing LIME with a global method (B) and misattributing SHAP to visualizing decision trees (D).

Answer

LIME: Global interpretability method that provides feature importance rankings across entire dataset.

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SHAP: A method for visualizing individual decision trees.

Question 3

Match each ML pipeline component to its description.

Accepted Answer

Data ingestion: Collecting and importing raw data from various sources.. The correct matches are: Data ingestion → Collecting and importing raw data from various sources. Data validation → Checking data quality, integrity, and schema compliance. Feature engineering → Transforming raw data into features that improve model performance. The remaining options are incorrect: Model training is not about assessing model performance (that's evaluation); Model evaluation is not about deploying a model (that's deployment); Model deployment is not about transforming raw data into features (that's feature engineering).

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Model training: The process of assessing model performance using metrics.

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Model evaluation: The process of deploying a model into production.

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Model deployment: The process of transforming raw data into features.

Question 4

A company uses Vertex AI Vector Search (Matching Engine) for a product recommendation system. The product embeddings are updated hourly. Which index update method should they use to ensure low latency for new items?

Accepted Answer

Use streaming updates to add new embeddings incrementally. Vertex AI Vector Search supports streaming updates, allowing new embeddings to be added incrementally without rebuilding the entire index. This ensures low latency for new items by making them searchable almost immediately after update, which is critical for hourly refresh cycles where batch rebuilds would introduce significant delay.

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Batch rebuild the index every hour

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Create a new index each hour and swap endpoints

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Use brute-force index to simplify updates

Question 5

What is the purpose of the 'importer' component in Vertex AI Pipelines?

Accepted Answer

To import existing ML artifacts (e.g., models, datasets) into a pipeline as inputs.. The Importer component in Vertex AI Pipelines (a Kubeflow Pipelines component) brings existing ML artifacts — such as pre-trained models, datasets, or other resources already registered in Vertex AI — into a pipeline as inputs. It creates an artifact reference so downstream components can consume it without regenerating it. This is essential for reusing models or datasets across pipeline runs.

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To import data from external sources into the pipeline.

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To import Python libraries into the pipeline environment.

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To import pipeline definitions from other projects.

Question 6

A data scientist wants to track the lineage of a dataset used in a training run. Which Vertex AI feature should they use?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the correct choice because it is specifically designed to track the lineage of datasets, models, and other artifacts throughout the ML lifecycle. It records metadata about each step in a pipeline, including the source dataset used for a training run, enabling full provenance tracking. This allows data scientists to trace back which data was used, how it was transformed, and which model version it produced.

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Vertex AI Feature Store

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Vertex AI Experiments

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Vertex AI Model Registry

Question 7

A data scientist uses Vertex AI Workbench to train a model and then deploys it to an endpoint. They want to automate the retraining and redeployment pipeline when new data arrives. Which service should they use?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the managed orchestration service for ML workflows on Google Cloud, designed to automate training, evaluation, and deployment steps as a reproducible DAG. It integrates natively with Vertex AI endpoints and supports triggers from new data events, making it the correct choice for automating retraining and redeployment. Cloud Composer can orchestrate, but Vertex AI Pipelines is purpose-built for ML and requires less overhead for this use case.

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Cloud Composer

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Cloud Scheduler

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Cloud Functions

Question 8

Your company runs a high-traffic web application that serves the same machine learning model prediction for many identical requests (e.g., product recommendations for the same user profile). You want to reduce latency and load on the prediction endpoint by caching responses. Which Google Cloud service should you use?

Accepted Answer

Cloud Memorystore. Cloud Memorystore (B) is correct because it provides a managed in-memory cache (Redis or Memcached) that can store the results of identical prediction requests, reducing latency and load on the prediction endpoint. By caching responses keyed on the user profile or request parameters, subsequent identical requests can be served directly from Memorystore in microseconds, avoiding redundant model inference.

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Cloud CDN

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Cloud Spanner

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BigQuery

Question 9

Your team has deployed a model on Vertex AI endpoints. You need to monitor the prediction latency to ensure it meets a 99th percentile SLO of 500ms. You want to set up an alert if the latency exceeds this threshold. Which metric should you use?

Accepted Answer

The 99th percentile of the `prediction/online/response_latencies` metric.. The `prediction/online/response_latencies` metric in Vertex AI provides a distribution of latency values, allowing you to query the 99th percentile directly. This aligns with the SLO requirement to monitor the tail latency, not the average or maximum, ensuring that the worst-case performance for 1% of requests stays under 500ms.

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The number of prediction requests that timeout.

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Average prediction latency from the endpoint's logs.

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The maximum prediction latency from the endpoint's monitoring dashboard.

Question 10

Which of the following is a best practice when designing idempotent pipeline components in Vertex AI?

Accepted Answer

Pass data through Cloud Storage URIs rather than in-memory.. Passing data through Cloud Storage URIs ensures that component outputs are stored persistently and can be retrieved by downstream components, even if the original component instance is terminated or scaled down. This aligns with the principle of idempotency because the same input will always produce the same output stored at the same URI, and re-running the component will not cause side effects or data loss. In contrast, in-memory data is ephemeral and tied to a specific runtime instance, breaking idempotency across retries or parallel executions.

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Use global variables to share state between components.

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Write component outputs to a database with timestamps.

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Use the same output name for all runs to avoid duplication.