PMLE Practice Test 4 — 10 Questions

Question 1

An ML team uses Vertex AI Pipelines to automate model retraining. The pipeline includes a step that queries BigQuery to create a training dataset. The team notices that the pipeline fails intermittently with a '403 Exceeded rate limits' error. What is the most likely cause and solution?

Accepted Answer

The pipeline is issuing too many concurrent queries; use a BigQuery reservation to guarantee slot capacity. The 403 'Exceeded rate limits' error in BigQuery indicates that the project is hitting the concurrent query rate limit or the rate of bytes read per second. Using a BigQuery reservation guarantees dedicated slot capacity, which prevents rate-limit errors by ensuring the pipeline has consistent compute resources regardless of other workloads in the project. This is the most direct solution because rate limits are enforced at the project level based on available slots, and a reservation provides a fixed number of slots that bypass those limits.

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The training dataset is too large; partition the table and query only the latest partition

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The pipeline step timeout is too short; increase the timeout to 30 minutes

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The SQL query is inefficient; rewrite it using materialized views

Question 2

Which TWO actions are appropriate when you detect that a production model's prediction distribution has shifted significantly from the training distribution?

Accepted Answer

Retrain the model using the most recent data. Option C is correct because retraining the model on the most recent data directly addresses the distribution shift by adapting the model to the new data patterns. This is a standard practice in MLOps when the shift is confirmed and the cause is understood, ensuring the model remains accurate and reliable in production.

Answer

Immediately roll back to the previous model version

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Increase logging for future predictions

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Reduce the traffic to the model to minimize impact

Question 3

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

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

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A misconfiguration in the autoscaling policy

Question 4

You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?

Accepted Answer

A recent code change that loads the entire dataset into memory before processing. Option A is correct because a code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.

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Increase in model size due to retraining

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Decrease in the number of worker machines

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Increase in input data size

Question 5

A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?

Accepted Answer

Copy the new_data table to the US region using the BigQuery UI or CLI. Option B is correct because the simplest fix is to move the new_data table to the same region as the model (US). BigQuery ML requires that the model and the data used for predictions reside in the same multi-region or regional location. Copying the table via the BigQuery UI or CLI (e.g., `bq cp`) is a straightforward, no-code operation that avoids retraining or exporting the model.

Answer

Recreate the model in the EU region using the same training data

Answer

Enable cross-region query in BigQuery settings

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Export the model from US and import it to EU

Question 6

An ML engineer is scaling a prototype to production using Vertex AI Pipelines. The pipeline includes data validation, preprocessing, training, and deployment steps. They want to ensure that the pipeline can be reproduced and audited. What is the best practice?

Accepted Answer

Define the pipeline using Kubeflow Pipelines SDK and run it on Vertex AI Pipelines.. Using a fully managed pipeline service like Vertex AI Pipelines automatically tracks artifacts, parameters, and lineage, ensuring reproducibility and auditability. Option A is not a service; Option B is about environment consistency but does not provide built-in tracking. Option D is about dependencies but not the pipeline orchestration.

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Use a Docker container with fixed tags and manually record runs.

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Store all data and models in a single Cloud Storage bucket with no versioning.

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Pin all library versions in a requirements.txt file.

Question 7

An e-commerce company uses a recommendation model deployed on Vertex AI Endpoints. The model's latency increases gradually over two weeks, causing timeouts. The model is served using a custom container. What is the most likely root cause and corrective action?

Accepted Answer

The custom container has a memory leak; implement memory monitoring and set container resource limits.. A gradual increase in latency over two weeks, without a sudden spike, strongly indicates a memory leak in the custom container. As the leak accumulates, the container's garbage collection becomes less effective, leading to increased GC pauses and eventual timeouts. Setting resource limits and monitoring memory usage can prevent the container from exhausting host memory and causing performance degradation.

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The model is receiving more traffic; scale the number of replicas.

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The Vertex AI endpoint has changed its URL; update the client application.

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The model file size has grown due to feature engineering; reduce feature count.

Question 8

An ML engineer is using Vertex AI Pipelines with Kubeflow Pipelines SDK (KFP) to orchestrate a training and deployment workflow. They want to reuse a custom component across multiple pipelines. The component is defined in a Python file 'preprocess.py' that includes a function decorated with @kfp.components.create_component_from_func. How should they package this component for reuse?

Accepted Answer

Import the preprocess module and call create_component_from_func on the function, then use the resulting component in pipeline definitions.. Option A is correct because the recommended way to reuse a custom component defined via `@kfp.components.create_component_from_func` is to import the Python module containing the decorated function and call `create_component_from_func` on that function in each pipeline definition. This creates a reusable component object that can be used directly in the pipeline's `@dsl.pipeline` definition without additional packaging steps. The KFP SDK treats the function as the source of truth, and re-importing ensures the component logic is always current.

Answer

Save the component as a YAML file using kfp.components.ComponentStore and load it in other pipelines.

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Compile the pipeline that uses the component into a JSON file and upload it to Vertex AI.

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Build a custom container image with the function and use it as a base image in other pipelines.

Question 9

You are an ML engineer at a large e-commerce company. Your team has developed a product recommendation model using TensorFlow and deployed it on Vertex AI Endpoints for real-time inference. The model is retrained weekly using a Vertex AI Pipeline that reads new user interaction data from BigQuery, trains the model, evaluates it, and deploys the new version to the endpoint with a traffic split: 10% to the new model and 90% to the previous champion model. Recently, the team noticed that the new model's online prediction latency has increased significantly (from 50ms to 200ms) after deployment, causing timeouts for some requests. The training code has not changed, and the model size is similar. The pipeline uses a custom container with the same TensorFlow Serving image as before. The deployment step uses the same machine type (n1-standard-4) for the endpoint. What is the most likely cause of the latency increase?

Accepted Answer

The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation.. Option C is correct because the pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation. This additional operation runs during inference on Vertex AI Endpoints, increasing the per-request latency from 50ms to 200ms, even though the model architecture and size remain unchanged. The custom container and machine type are identical, so the latency increase must stem from a change in the serving graph itself.

Answer

The endpoint is using a machine type that is not optimized for the new model's computation.

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The new model has a significantly different architecture that requires more computation.

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The new model is experiencing data skew because the training data distribution has changed.

Question 10

A company implements an ML pipeline using Vertex AI Pipelines. The pipeline trains a model using custom training jobs and then deploys it to an endpoint. The team notices that the endpoint occasionally serves an older model version for a few minutes after a new pipeline run completes. What is the most likely cause?

Accepted Answer

The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.. D is correct because Vertex AI endpoints can be configured with a canary (gradual) traffic rollout strategy. When a new model is deployed, traffic is shifted incrementally from the old model to the new one over a specified duration. During this rollout window, the old model continues to serve a fraction of requests, which explains why users occasionally see the older model version for a few minutes after the pipeline completes.

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The new model artifact is temporarily unavailable, so the endpoint falls back to the previous version.

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The prediction cache is returning cached results from the old model.

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The pipeline failed to update the endpoint with the new model ID.