PMLE Practice Test 30 — 15 Questions

Question 1

A team is building a feature pipeline for an ML model. They need to compute aggregate features over a sliding time window from streaming data. Which Google Cloud service is most appropriate for this task?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cloud Dataflow with sliding windows. Cloud Dataflow, based on Apache Beam, natively supports sliding time windows, making it the appropriate service for computing aggregate features over overlapping time intervals from streaming data. Fixed windows alone would not satisfy the sliding-window requirement, and the other services lack native sliding window aggregation capabilities.

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Cloud Pub/Sub for windowing logic

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BigQuery scheduled queries

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Cloud Functions with Pub/Sub triggers

Question 2

A team has a trained TensorFlow model running locally and wants to deploy it for low-latency online predictions on Google Cloud. Which service should they use?

Accepted Answer

Vertex AI Prediction. Vertex AI Prediction is the correct choice because it is a fully managed service designed specifically for deploying trained ML models for online (real-time) prediction with low latency. It supports importing TensorFlow SavedModel artifacts and automatically scales the serving infrastructure, including GPU/TPU support, to handle request traffic while providing built-in monitoring and explainability features.

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AI Platform Training

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Cloud Run

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Cloud Functions

Question 3

You are deploying a scikit-learn model to Vertex AI for online prediction. The model was trained on a dataset with numerical features and expects input data in a specific JSON format. You have created a custom container that serves the model using a Flask app. After deploying the model to a Vertex AI Endpoint, you send a prediction request with a JSON payload, but the response is an error indicating that the input format is invalid. What is the most likely reason for this error?

Accepted Answer

The custom container's Flask app does not parse the incoming JSON correctly because it expects a different key or structure than what was sent.. The correct answer is that the custom container's Flask app does not parse the incoming JSON correctly. Vertex AI passes the raw request to the container, so the container must implement logic to extract and format the input as expected by the model. If the JSON structure sent by the client does not match what the container expects, an input format error occurs. Ensuring alignment between the client request and container preprocessing is essential.

Answer

The Vertex AI Endpoint requires the input data to be base64-encoded.

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The Vertex AI Endpoint automatically converts the input to a TensorFlow tensor, which is incompatible with scikit-learn models.

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The model artifact was not uploaded to Cloud Storage, so the container cannot load the model and returns an input format error.

Question 4

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. Vertex AI Model Registry supports model cards, which are structured documents that capture training data, feature importance, evaluation metrics, intended use, and limitations. Model cards are designed specifically for automated, standardized model documentation and governance. This makes Model Registry with model cards the correct feature for the stated requirement.

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Vertex AI Metadata

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Vertex AI Explainable AI

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Vertex AI Pipelines

Question 5

An ML engineer has a Vertex AI pipeline that trains a model and then evaluates it. The engineer wants the pipeline to automatically deploy the model to an endpoint only if the evaluation metric (e.g., accuracy) exceeds a threshold. The pipeline is defined using the Kubeflow Pipelines SDK. Which approach should the engineer use to implement this conditional deployment?

Accepted Answer

Use a Vertex AI Pipelines condition with a comparison of the metric output to the threshold, and place the deployment component inside the condition's 'then' branch.. Conditional deployment in Vertex AI Pipelines is achieved by using dsl.Condition to evaluate a metric against a threshold. The deployment component is placed inside the condition's true branch, ensuring it only executes when the metric meets the requirement. This provides a clean, orchestrated way to gate deployment without external services or manual intervention.

Answer

Set the deployment component's 'condition' parameter to the metric value, and rely on the component to skip itself if the value is below threshold.

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Configure the pipeline to always deploy, and use a Cloud Function triggered by a Pub/Sub message from the evaluation step to roll back if the metric is below threshold.

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Use a Vertex AI Model Evaluation component and set its 'deploy' flag to true, which automatically deploys if the metric meets the threshold.

Question 6

An ML engineer is building a Vertex AI pipeline that trains a model and then evaluates it. The evaluation component must compare the new model's accuracy against a fixed threshold and, if the model passes, trigger a downstream deployment component. The engineer wants to avoid running the deployment component when the model fails. Which approach should the engineer use?

Accepted Answer

Define a condition on the deployment component using the evaluation metric output from the evaluation component.. Conditional execution in Vertex AI Pipelines is implemented using the dsl.Condition construct, which allows a component to run only if a specified condition is met. By referencing the evaluation metric output, the pipeline can automatically decide whether to deploy the model. This approach avoids running unnecessary components and keeps the pipeline logic self-contained.

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Use a pipeline parameter to pass the evaluation result to the deployment component and let the deployment component decide whether to run.

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Configure the evaluation component to raise an exception if the model fails, which will stop the entire pipeline before reaching the deployment component.

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Create a separate pipeline that only contains the deployment component and trigger it manually after reviewing the evaluation results.

Question 7

A team wants to serve a large PyTorch model (3 GB) for online predictions with low latency. Which THREE actions should they take?

Accepted Answer

Use a custom container that preloads the model into memory.. Option A is correct because a custom container that preloads the 3 GB PyTorch model into memory eliminates the per-request model-loading overhead, which is essential for low-latency online predictions. Option C is correct because attaching a GPU accelerator provides the parallel compute throughput PyTorch inference needs, drastically reducing latency for a large model compared to CPU-only serving. Option D is correct because TorchScript (tracing/scripting for optimized execution) and quantization (e.g., FP16/INT8) reduce model size and inference cost, directly lowering latency while preserving online serving. Option B is wrong because batch prediction is asynchronous and high-throughput, not low-latency online serving, so it contradicts the stated requirement. Option E is wrong because multi-region deployment with Cloud Load Balancing improves global availability and proximity, but does not address the model's size or per-inference latency for online predictions.

Answer

Use batch prediction instead of online prediction.

Answer

Deploy in multiple regions with Cloud Load Balancing.

Question 8

An ML engineer is building a Vertex AI pipeline that includes a component to train a model. The component takes a long time to run and occasionally fails due to transient errors (e.g., network timeouts). The engineer wants to automatically retry the component a few times if it fails. How should the engineer configure this?

Accepted Answer

Set the retry policy on the pipeline task using the 'retry' parameter in the component's task definition, specifying the number of retries and backoff.. To automatically retry a component on failure, the engineer should set a retry policy on the task. This is done by specifying the retry parameter in the task definition, including max_retries and backoff settings. This leverages Vertex AI Pipelines' built-in retry mechanism, which is designed for transient failures.

Answer

Use a Cloud Scheduler job to trigger the pipeline again if it fails, and rely on the pipeline to resume from the failed step.

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Set the pipeline's execution timeout to a high value and hope that the component succeeds on its own.

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Configure the component to catch exceptions and loop internally until it succeeds, with no limit on retries.

Question 9

An ML engineer is designing a Vertex AI pipeline that trains a model, evaluates it, and conditionally deploys it only if the evaluation metric meets a threshold. The pipeline must pass the evaluation metric from the evaluation component to a downstream conditional. Which Vertex AI Pipelines feature should the engineer use to implement this flow?

Accepted Answer

Use the output of the evaluation component as an input parameter to a condition in the pipeline definition, referencing the component's output parameter.. In Vertex AI Pipelines, component outputs can be promoted to pipeline parameters, which can then be used in conditions to control downstream execution. The evaluation component should output the metric as a parameter, and the condition compares it to the threshold. This maintains a single, traceable pipeline run and avoids external services.

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Use the Vertex AI Model Registry to compare the new model's metric with the deployed model's metric and let the registry automatically deploy if better.

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Write the metric to a Cloud Storage file and use a Cloud Function to trigger a separate pipeline for deployment.

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Store the metric in a Vertex AI Experiment run and then use the Experiment's metric in the condition.

Question 10

Your team trains models in a shared Vertex AI project, and multiple engineers run pipelines against the same BigQuery training tables. A reviewer needs to reproduce the exact dataset used to train a model six weeks ago, but the source tables have been overwritten many times since. Which BigQuery capability should you have used to make each training snapshot reproducible?

Accepted Answer

BigQuery table snapshots created before each training run. Reproducing a past training run requires an immutable copy of the data as it existed then. Table snapshots provide exactly that: a point-in-time, read-only reference retained independently of later writes, so the reviewer can rebuild the training dataset even after many overwrites. Time travel, materialized views, and streaming audit tables all reflect or lose the historical state.

Answer

BigQuery streaming inserts into a dedicated audit table

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BigQuery time travel queries against the live table

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BigQuery materialized views over the training tables

Question 11

An ML engineer is authoring a Vertex AI pipeline where a custom training component must read a dataset from a BigQuery table and write the trained model to a Cloud Storage bucket. The engineer wants the component to be reusable across projects and environments without hardcoding project IDs or bucket names. Which design should the engineer use?

Accepted Answer

Pass the BigQuery table URI and the Cloud Storage output URI as component input parameters, and let the pipeline caller supply them at runtime.. Component reusability in Vertex AI Pipelines comes from parameterizing all environment-specific values as inputs. Passing the BigQuery table URI and Cloud Storage output URI as component inputs lets the same component definition run in any project or environment, with the pipeline caller providing concrete values. The other options embed environment-specific values in the container or rely on implicit defaults, which breaks portability and can cause cross-environment mistakes.

Answer

Use the Vertex AI SDK's aiplatform.init() with no arguments inside the component and rely on the default project and bucket.

Answer

Read the project ID and bucket name from environment variables set inside the component's container image at build time.

Answer

Hardcode the production project ID and bucket name in the component, then override them with a pipeline-level parameter only when running in non-production.

Question 12

A data analyst wants to use low-code ML to analyze text data. Which TWO Google Cloud services are appropriate?

Accepted Answer

Cloud Natural Language API. Cloud Natural Language API is a low-code ML service that provides pre-trained models for analyzing text, including sentiment analysis, entity recognition, and syntax analysis, without requiring custom model training. It is appropriate for a data analyst who wants to quickly extract insights from text data using simple API calls.

Answer

Vertex AI Workbench

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Document AI

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BigQuery ML for sentiment

Question 13

An ML engineer is building a Vertex AI pipeline that must run a custom training component for each of 12 hyperparameter combinations. The component is defined as a custom Python function (Lightweight Python component). The engineer wants each combination to run as a separate parallel task so the pipeline completes faster, and wants the pipeline to fail fast if any single trial fails. Which approach should the engineer take?

Accepted Answer

Use a ParallelFor loop over the hyperparameter list and set the component's retry policy to 0.. Vertex AI Pipelines supports dsl.ParallelFor to fan out a list of values into parallel component tasks in one DAG, which is the idiomatic way to run 12 hyperparameter combinations concurrently. Setting retries to 0 on the component ensures a failed trial stops the pipeline rather than being retried and hidden. The other approaches either collapse trials into a single job, serialize them, or split them across pipelines, none of which meet the parallel fan-out and fail-fast requirements.

Answer

Wrap the training component in a single Vertex AI CustomJob with a hyperparameter tuning job specification and let the service manage trials.

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Create 12 separate pipelines, one per hyperparameter combination, and schedule them with Cloud Scheduler at the same time.

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Define a sequential for-loop in the pipeline function that calls the training component 12 times in order.

Question 14

An ML engineer is using Vertex AI Pipelines to orchestrate a workflow that includes a data preprocessing step, a training step, and an evaluation step. The evaluation step must run only if the training step succeeds and the evaluation metric meets a threshold. The engineer wants to define this logic natively in the pipeline without writing a custom component that exits with a specific code. Which Vertex AI Pipelines feature should they use?

Accepted Answer

Use the kfp.dsl.Condition context manager to conditionally execute the evaluation step based on the metric value.. Vertex AI Pipelines supports conditional execution through the kfp.dsl.Condition context manager. This allows you to branch based on the value of a task output, such as an evaluation metric. You can wrap the evaluation step in a Condition that checks if the metric exceeds a threshold. The other options either misuse trigger policies, misunderstand exit handlers, or rely on static pipeline parameters, none of which provide runtime conditional execution based on a metric.

Answer

Use a pipeline parameter of type 'bool' to control whether the evaluation step runs, and set it based on the metric before submitting the pipeline.

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Configure the pipeline's 'exit handler' to check the metric and either continue or fail the pipeline.

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Set the evaluation step's 'trigger' policy to 'on_success' and pass the metric threshold as a pipeline parameter.

Question 15

A team uses Vertex AI Pipelines with a custom training component that reads data from a BigQuery table. They need to ensure that a new pipeline run uses a specific snapshot of the training data for reproducibility. Which approach should they take?

Accepted Answer

Create a BigQuery table snapshot of the training data and pass the snapshot name to the training component as an input artifact.. To ensure reproducibility, the training data must be immutable. A BigQuery table snapshot provides a point-in-time copy that does not change even if the source table is updated. Passing the snapshot name to the training component as an input artifact also integrates with Vertex AI Pipelines lineage tracking, making the data reference explicit and stable.

Answer

Pass the BigQuery table name and a WHERE clause that filters on a timestamp column to the training component, and record the query in the pipeline parameters.

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Export the BigQuery table to a Cloud Storage bucket in CSV format and pass the Cloud Storage URI to the training component.

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Use BigQuery's time travel feature by adding FOR SYSTEM_TIME AS OF a fixed timestamp to the query in the training component.