PMLE Practice Exam 3 — 20 Questions

Question 1

A machine learning engineer wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they use?

Accepted Answer

Python function component with @dsl.component. The `@dsl.component` decorator in KFP SDK allows you to define a lightweight Python function component that runs custom code without requiring a container image. It automatically generates a container specification from the function's dependencies, making it ideal for simple, non-containerized pipeline steps.

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Importer component

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Container component

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Vertex AI Training job

Question 2

A data scientist needs to scale a prototype deep learning model to train on a massive dataset using multiple GPUs. Which three strategies are essential for efficient distributed training? (Select THREE)

Accepted Answer

Implement data parallelism.. Option B is correct because data parallelism is the foundational strategy for multi-GPU training: the model is replicated on each GPU and each replica processes a different shard of the massive dataset, with gradients aggregated across workers to scale throughput. Option C is correct because at multi-GPU scale the input pipeline can easily starve the accelerators; using tf.data.Dataset with parallel reads (num_parallel_reads / interleave) and prefetching (prefetch(AUTOTUNE)) overlaps data loading and preprocessing with GPU computation so the GPUs are not idle. Option D is correct because synchronous gradient updates (e.g., all-reduce via NCCL, as in tf.distribute.MirroredStrategy) aggregate gradients from all workers before applying the optimizer step, which keeps replicas consistent and yields stable, reproducible convergence for deep learning training. Option A is not correct because a single large batch size is not itself an essential distributed-training strategy; batch size is a tuning choice, and naively enlarging it can hurt convergence and may not fit in memory. Option E is not correct because asynchronous gradient updates are an alternative to, not a requirement for, efficient distributed training, and they can introduce stale gradients that degrade model accuracy and reproducibility.

Answer

Use a single large batch size across all workers.

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Use asynchronous gradient updates to reduce communication overhead.

Question 3

Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?

Accepted Answer

Write a cloudbuild.yaml that builds a training container and submits a Vertex AI PipelineJob. Option A is correct because a cloudbuild.yaml file is the declarative configuration Cloud Build uses to define build steps, and one of those steps can build a custom training container image (e.g., via docker build) and then submit a Vertex AI PipelineJob using the gcloud ai pipelines run command or the Vertex AI SDK. Option D is correct because Vertex AI Pipelines is the native service for orchestrating ML workflow steps such as training, evaluation, and deployment as a directed acyclic graph, and Cloud Build can invoke that pipeline as part of CI/CD, giving a fully automated, reproducible ML pipeline. Option E is correct because a Cloud Build trigger bound to the source repository (e.g., a GitHub or Cloud Source Repositories trigger on push or pull request) is what actually initiates the automated build/pipeline run on commits, which is the core of CI/CD automation. Option B is not appropriate because Cloud Functions is not the intended mechanism for retraining models on build completion; Cloud Build triggers or Pub/Sub notifications are the correct integration points, and this adds unnecessary complexity. Option C is not appropriate because polling for build artifacts with Cloud Scheduler is an anti-pattern; event-driven triggers (Cloud Build triggers or Pub/Sub) are preferred over scheduled polling for reacting to new builds.

Answer

Use Cloud Functions to retrain the model each time a build completes

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Set up a Cloud Scheduler job to poll for new build artifacts

Question 4

A data science team uses Vertex AI Workbench and wants to share notebooks with version history. Which service should they use?

Accepted Answer

Cloud Source Repositories. Cloud Source Repositories (CSR) is the correct choice because it provides Git-based version control for notebooks, enabling teams to track changes, collaborate, and maintain a full version history. Vertex AI Workbench integrates natively with CSR, allowing users to clone, commit, and push notebook files directly from the JupyterLab interface, which is essential for collaborative development with revision tracking.

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Artifact Registry

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Cloud Storage

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Data Catalog

Question 5

A data science team has trained a TensorFlow model on-premises using a large dataset. When they try to deploy the model to Vertex AI for online predictions, the deployed model fails to start with a ‘MemoryError’. The model artifact is 2 GB, and the machine type is n1-standard-4 (15 GB RAM). What is the most likely cause?

Accepted Answer

The model is too large for the machine's memory, causing an out-of-memory (OOM) error during loading.. The model artifact is 2 GB, and loading it into memory on an n1-standard-4 machine (15 GB RAM) can still cause a MemoryError. TensorFlow models often require additional memory for graph construction, intermediate tensors, and framework overhead, which can easily exceed the available RAM, especially when the model is loaded entirely into memory before serving.

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The model is stored in a regional bucket and the Vertex AI endpoint is in a different region.

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The machine type does not support TensorFlow models larger than 1 GB.

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The model file is corrupted or missing dependencies, causing a crash.

Question 6

Refer to the exhibit. An alert policy is configured to trigger when prediction latency exceeds 500 ms for 5 consecutive minutes. The team is experiencing many false positive alerts during brief latency spikes. Which adjustment would most effectively reduce false positives while still detecting prolonged latency issues?

Accepted Answer

Increase the duration to 30 minutes. Increasing the duration from 5 to 30 minutes (Option C) directly addresses the problem of false positives from brief latency spikes by requiring the latency to exceed 500 ms for a longer continuous period before triggering an alert. This ensures that only sustained, prolonged latency issues—not transient spikes—activate the policy, aligning with the goal of detecting genuine degradation while ignoring noise.

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Change the comparison to less than

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Add a condition that CPU utilization is also high

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Increase the threshold to 1000 ms

Question 7

A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

Accepted Answer

Delta Lake on Dataproc. Delta Lake on Dataproc provides ACID transactions on cloud storage data lakes, enabling concurrent reads/writes with consistency.

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BigQuery table snapshots

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DVC (Data Version Control)

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Vertex AI Feature Store

Question 8

An ML pipeline must run a set of preprocessing tasks for each data shard in parallel. Which KFP SDK features should they use to implement this? (Choose two.)

Accepted Answer

dsl.ParallelFor. dsl.ParallelFor (A) is correct because it is the KFP SDK construct that iterates over a list (such as the set of data shards) and fans out the loop body into parallel task executions, which is exactly what is needed to run preprocessing per shard concurrently. dsl.Collected (C) is correct because it is used with dsl.ParallelFor to gather the outputs of all parallel iterations into a single list, allowing downstream pipeline steps to consume the aggregated results of the per-shard preprocessing. dsl.PipelineParam (B) is not the mechanism for parallel iteration; it only represents a runtime parameter passed into a pipeline or component. dsl.Condition (D) implements conditional branching (if/else) rather than parallel fan-out, and dsl.ExitHandler (E) defines cleanup logic that runs when a scope exits, neither of which provides the required parallel execution over shards.

Answer

dsl.PipelineParam

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dsl.Condition

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dsl.ExitHandler

Question 9

A company has a large dataset of labeled images (e.g., different species of plants). They want to train a custom image classification model with minimal effort and no prior ML experience. Which Google Cloud service should they use?

Accepted Answer

AutoML Vision. AutoML Vision is the correct choice because it allows users with no prior ML experience to train a custom image classification model using a simple graphical interface, requiring only labeled images as input. It automates model architecture search, hyperparameter tuning, and deployment, minimizing manual effort while delivering a production-ready model.

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Cloud TPU

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Vertex AI Workbench with a custom TensorFlow model

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Vision API

Question 10

Match each MLOps practice to its description.

Accepted Answer

CI/CD: Automates the processes of building, testing, and deploying ML models.. This matching question requires associating each MLOps practice (CI/CD, Model Monitoring, Data Versioning) with its correct description. The correct matches are: CI/CD automates building, testing, and deploying ML models (option A); Model Monitoring continuously observes model performance and detects data drift (option C); Data Versioning manages and tracks changes to datasets over time (option E). Common mistakes include confusing CI/CD with monitoring or versioning, or assigning model monitoring to data versioning tasks. The wrong options (B, D, F) are incorrect because they swap responsibilities: B wrongly attributes monitoring to CI/CD, D assigns versioning to monitoring, and F assigns CI/CD to versioning.

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CI/CD: Continuously observes model performance and detects data drift.

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Model Monitoring: Manages and tracks changes to datasets over time.

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Data Versioning: Automates the processes of building, testing, and deploying ML models.

Question 11

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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.