PMLE Practice Questions 2 — 50 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.

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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).

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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.

Question 11

A company wants to analyze videos to detect objects and track their movement over time. Which TWO Google Cloud services are suitable for this task?

Accepted Answer

AutoML Video. AutoML Video supports object tracking, and Video Intelligence API provides both object detection and tracking. AutoML Vision is for images only, Natural Language for text, and Speech-to-Text for audio.

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AutoML Vision

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Speech-to-Text

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Natural Language API

Question 12

Which Vertex AI feature allows you to reduce the size of a trained model to improve inference speed on edge devices without significant accuracy loss?

Accepted Answer

Vertex AI Model Optimization. Vertex AI Model Optimization is the correct feature because it provides model quantization, pruning, and distillation techniques specifically designed to reduce model size and improve inference latency on edge devices. This service applies post-training quantization (e.g., FP32 to INT8) and structured weight pruning to shrink the model footprint while maintaining accuracy within acceptable thresholds, directly addressing the need for efficient deployment on resource-constrained hardware.

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Vertex AI Model Monitoring

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Vertex AI Matching Engine

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Vertex AI Continuous Training

Question 13

A machine learning engineer is building a Vertex AI pipeline that uses a pre-built AutoML Tables component to train a classification model. The pipeline also includes a conditional step that deploys the model to an endpoint only if the evaluation metrics exceed a threshold. Which KFP feature should be used to implement the conditional deployment?

Accepted Answer

dsl.Condition. The `dsl.Condition` feature from KFP (Kubeflow Pipelines) is specifically designed to conditionally execute pipeline steps based on the output of a previous component. In this scenario, the AutoML Tables component produces evaluation metrics; `dsl.Condition` allows the pipeline to check whether those metrics exceed a threshold and, if true, run the deployment step. This is the correct, native KFP construct for implementing branching logic within a pipeline.

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dsl.ParallelFor

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dsl.ExitHandler

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dsl.Collected

Question 14

When distributing training across multiple workers using Vertex AI Training, how should the team share the training dataset?

Accepted Answer

Use Cloud Storage. Vertex AI Training workers need shared, concurrent read access to the training dataset without manual replication. Cloud Storage (GCS) is the recommended and fully integrated solution because it provides a distributed, highly available object store that all workers can read from in parallel via the `tf.io.gfile` API or GCS connector, eliminating data duplication and ensuring consistency across the cluster.

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Copy the dataset to each worker's local disk

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Use NFS

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Use Google Drive

Question 15

Which Vertex AI service is best suited for finding similar items in a large dataset based on embedding vectors, such as product recommendations or image similarity search?

Accepted Answer

Vertex AI Matching Engine. Vertex AI Matching Engine is specifically designed for high-performance vector similarity search (also known as approximate nearest neighbor search) using embedding vectors. It scales to billions of vectors and is ideal for use cases like product recommendations and image similarity search, where you need to find the most similar items based on dense vector representations.

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Vertex AI Prediction Endpoint

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Vertex AI Model Monitoring

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Vertex AI Feature Store

Question 16

An engineer needs to compile a Kubeflow Pipeline defined in Python to a JSON format that can be run on Vertex AI Pipelines. Which command should they use?

Accepted Answer

kfp.compiler.Compiler().compile(pipeline_func, 'pipeline.json'). The Kubeflow Pipelines SDK provides the `kfp.compiler.Compiler().compile()` method to convert a Python-based pipeline function into a JSON or YAML format that is compatible with Vertex AI Pipelines. This JSON representation defines the pipeline's components, dependencies, and execution graph, enabling it to be submitted to Vertex AI for orchestration. The `compile()` method is the standard way to produce a portable pipeline specification from Python code.

Answer

gcloud ai pipelines compile command.

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kfp.Client().upload_pipeline()

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dsl.pipeline decorator automatically compiles at runtime.

Question 17

A company wants to classify support ticket text into categories. They have labeled historical tickets. Which Google Cloud service allows them to train a custom classification model with no code?

Accepted Answer

AutoML Natural Language. AutoML Natural Language (now part of Vertex AI) is the correct service because it enables users to train custom text classification models using labeled data without writing any code. It provides a no-code interface for uploading datasets, training models, and evaluating performance, making it ideal for classifying support ticket text into custom categories.

Answer

Vertex AI Matching Engine

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Cloud Natural Language API

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Document AI

Question 18

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

Accepted Answer

Human-in-the-Loop (HITL). Human-in-the-Loop (HITL) in Document AI allows you to route documents that the model processes with low confidence to human reviewers for validation or correction before finalizing. This directly matches the requirement to have a human review any documents the model cannot process with high confidence.

Answer

AutoML Tables model retraining

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Cloud DLP for data inspection

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Increase the number of processors

Question 19

A developer wants to add text translation to a mobile app. They need to translate user-generated content into multiple languages, and latency is critical. Which pre-built API should they use?

Accepted Answer

Translation API. Translation API provides fast, real-time translation for text. Natural Language API is for analysis. Text-to-Speech is for audio. Vision API is for images.

Answer

Vision API

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Text-to-Speech API

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Natural Language API

Question 20

A data science team is deploying a PyTorch model for real-time inference using Vertex AI Endpoints. The model requires a custom container with specific CUDA drivers and Python packages. They have created a Docker image and pushed it to Artifact Registry. The pipeline should automatically retrain the model every week and deploy the new version if it passes validation. However, the deployment step fails intermittently with the error 'The container image is not compatible with the machine type.' What is the most likely cause?

Accepted Answer

The container image requires GPU support but the machine type specified in the endpoint is a CPU-only machine.. The error 'The container image is not compatible with the machine type' indicates a mismatch between the container's hardware requirements and the machine type selected for the Vertex AI Endpoint. Since the custom container requires specific CUDA drivers, it is built for GPU acceleration. If the endpoint is configured with a CPU-only machine type (e.g., n1-standard-4), the container will fail to run because the GPU drivers cannot initialize, triggering this incompatibility error.

Answer

The service account does not have permission to pull the container from Artifact Registry.

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The container's health check endpoint is not responding correctly.

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The model artifact size exceeds the maximum allowed for the machine type.