PMLE Practice Exam 62 — 20 Questions

Question 1

An ML engineer is building a pipeline on Vertex AI Pipelines and wants to pass a dataset artifact from one component to another without incurring additional cost for intermediate storage. How should they define the input and output types?

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

Accepted Answer

Use the Dataset artifact type available in the KFP SDK for inputs and outputs.. Using the KFP SDK's Dataset artifact type allows Vertex AI Pipelines to manage the data as a lineage-tracked artifact, automatically handling the underlying GCS storage reference without incurring additional intermediate storage costs. The artifact type enables the pipeline to pass the metadata (URI, type, etc.) between components efficiently, leveraging the native artifact management of the KFP SDK.

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Store the data in BigQuery and pass the table reference.

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Define output as a string and pass the GCS path manually.

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Use in-memory Python objects as function return values.

Question 2

Which THREE of the following are recommended practices for model governance and lineage in Vertex AI?

Accepted Answer

Enable Vertex AI ML Metadata to track artifacts, executions, and contexts.. Vertex AI ML Metadata is a fully managed service that automatically tracks artifacts, executions, and contexts across the ML workflow. By enabling it, you create a lineage graph that records every step from data preparation to model deployment, which is essential for auditability and reproducibility. This is a core recommended practice for model governance because it provides an immutable, queryable history of all model-related activities.

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Store model artifacts in Cloud Storage with metadata in a database.

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Manually record model lineage in a spreadsheet.

Question 3

A data science team is designing a Vertex AI pipeline that includes a loop over a list of hyperparameter sets. They want to run training jobs in parallel for each hyperparameter set and then collect the results for comparison. Which two Kubeflow Pipelines SDK v2 features should they use? (Choose two.)

Accepted Answer

dsl.ParallelFor. `dsl.ParallelFor` is the Kubeflow Pipelines SDK v2 feature that enables iterating over a list of hyperparameter sets and executing the training tasks in parallel. This directly supports the team's requirement to run multiple training jobs concurrently for each hyperparameter configuration.

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dsl.If

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dsl.ExitHandler

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dsl.importer

Question 4

Which TWO options are best practices for building ML pipelines on Vertex AI?

Accepted Answer

Use custom container components to encapsulate reusable logic. Custom container components allow you to encapsulate reusable logic with specific dependencies, libraries, and environments, enabling consistent execution across pipeline steps. This is a best practice for building modular, maintainable ML pipelines on Vertex AI, as it decouples step logic from the pipeline orchestration and supports versioning and testing.

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Use Cloud Functions to execute individual pipeline steps

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Hardcode pipeline parameters in the component definitions

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Always use the same compute environment for training and serving to ensure consistency

Question 5

A company has deployed a TensorFlow model on Vertex AI Prediction for real-time inference. They notice that during peak hours, the prediction latency increases significantly, and some requests time out. The model requires GPU acceleration. Which action should they take to reduce latency and avoid timeouts?

Accepted Answer

Enable autoscaling with min replicas set to the base load and max replicas set to handle peak load, and ensure GPU quota is sufficient.. Enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also critical to prevent resource exhaustion. Option B is wrong because switching to a larger machine type with more vCPUs does not address GPU-bound inference latency; the bottleneck is GPU acceleration, not CPU. Option C is wrong because statically increasing replicas leads to over-provisioning and waste during off-peak hours, and cannot react quickly to sudden traffic spikes. Option D is wrong because Cloud Functions are serverless and do not support GPU acceleration; they add additional latency and are not suitable for real-time GPU inference.

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Switch to a larger machine type with more vCPUs.

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Increase the number of replicas in the Vertex AI Prediction endpoint statically to handle peak load.

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Use Cloud Functions to invoke the model asynchronously.

Question 6

A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?

Accepted Answer

ARIMA_PLUS. ARIMA_PLUS is the correct choice because it is specifically designed for time-series forecasting in BigQuery ML, handling seasonal patterns, trend decomposition, and automatic hyperparameter tuning. It models autoregressive (AR) and moving average (MA) components with seasonal differencing, making it ideal for historical sales data with strong seasonal cycles.

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MATRIX_FACTORIZATION

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BOOSTED_TREE_REGRESSOR

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K_MEANS

Question 7

You are using Vertex AI Vector Search to find nearest neighbors for a recommendation system. Your index is built on 10M embeddings and you need low-latency queries. You want to ensure that adding new embeddings does not require a full index rebuild. Which index type should you use?

Accepted Answer

ANN index with the 'streaming' update mode. The ANN index with 'streaming' update mode is correct because it supports real-time insertion of new embeddings without requiring a full index rebuild, which is essential for low-latency recommendation systems. This mode uses a separate unsearched buffer for new vectors and periodically merges them into the main index, enabling continuous updates while maintaining query performance.

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Brute-force index (exact neighbor search)

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ANN index with the 'batch' update mode

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Tree-AH index

Question 8

Your team is using Vertex AI Pipelines to build an automated training pipeline. You need to share the pipeline definition with another team so they can run it in their own project. Which format should you use?

Accepted Answer

Export the pipeline as a YAML file using the Kubeflow Pipelines SDK.. Vertex AI Pipelines is built on Kubeflow Pipelines, and the standard way to share a pipeline definition is to export it as a YAML file using the Kubeflow Pipelines SDK (`kfp.compiler.Compiler().compile()`). This YAML file contains the complete pipeline specification, including all components, dependencies, and execution order, and can be uploaded and run in any Vertex AI project without requiring the original Python code or build environment.

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Copy the pipeline artifacts to a Cloud Storage bucket and share the bucket.

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Package the pipeline as a Docker container image.

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Share the Python code that compiles the pipeline.

Question 9

An ML team is scaling a prototype to production. The data pipeline currently reads from Cloud Storage and transforms data with a custom Python script. They need to handle higher throughput and add monitoring. Which approach should they take?

Accepted Answer

Migrate the pipeline to Apache Beam on Dataflow with Cloud Monitoring. Apache Beam on Dataflow provides a unified programming model for batch and streaming data processing, enabling automatic scaling to handle higher throughput. Cloud Monitoring integrates natively with Dataflow to track pipeline metrics, latency, and error rates, addressing the monitoring requirement. This approach is purpose-built for production-grade data pipelines, unlike ad-hoc solutions.

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Deploy the Python script on a large Compute Engine instance with a cron job

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Rewrite the pipeline to use Pub/Sub and Cloud Functions for processing

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Use Cloud Composer to orchestrate the Python script at scale

Question 10

An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

Accepted Answer

Set up an A/B experiment comparing the model's recommendations against a baseline. An A/B experiment directly measures the model's real-world impact by comparing its CTR against a baseline (e.g., random or popularity-based recommendations). This isolates the model's performance from confounding factors like seasonality or user behavior changes, providing a causal estimate of its effectiveness.

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Run offline evaluation on a holdout dataset to confirm accuracy

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Increase the training dataset size by including data from two years ago

Question 11

A company uses Vertex AI Matching Engine for a product recommendation system. They need to update the index with new product embeddings every hour, but the index is used for online queries with low latency. Which index update strategy should they use?

Accepted Answer

Use streaming updates to insert new embeddings incrementally. Streaming updates in Vertex AI Matching Engine allow incremental insertion of new embeddings into an existing index without rebuilding it. This satisfies the requirement for hourly updates while maintaining low-latency online queries, as the index remains available and consistent during the update process.

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Use a hybrid approach with batch for daily full rebuild and streaming for hourly

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Use batch updates to replace the index every hour

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Recreate the index from scratch each hour

Question 12

An organization wants to trigger a Vertex AI pipeline whenever a new commit is pushed to the main branch of their Cloud Source Repository. The pipeline should retrain and evaluate the model. Which service should they use to detect the push event and start the pipeline?

Accepted Answer

Cloud Build trigger. Cloud Build triggers are designed to automatically invoke a build pipeline in response to events from Cloud Source Repository, such as a push to a specific branch. This allows the organization to directly start a Vertex AI pipeline for retraining and evaluation without additional infrastructure. Cloud Build triggers natively integrate with Cloud Source Repository, making them the simplest and most reliable choice for this event-driven workflow.

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Vertex AI Pipeline schedule

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Cloud Scheduler on a short interval

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Pub/Sub with push subscription to a Cloud Function

Question 13

You are using Vertex AI Matching Engine (Vector Search) to serve similarity search for an e-commerce product recommendation system. The index is updated daily with new product embeddings via a batch job. However, you notice that some new products are not appearing in the search results for up to 24 hours. You need to ensure that new products are discoverable within 1 hour of ingestion. What should you do?

Accepted Answer

Switch to a streaming update configuration for the index.. Vertex AI Matching Engine supports streaming updates to the index, which allows new embeddings to be added in near real-time (typically within minutes) without requiring a full batch rebuild. By switching to a streaming update configuration, new product embeddings become searchable within the 1-hour requirement, as the index is updated incrementally as data arrives.

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Deploy multiple index endpoints and use traffic splitting.

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Increase the frequency of the batch update job to run every 30 minutes.

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Use a brute-force index instead of an ANN index.

Question 14

A fintech company needs to deploy a TensorFlow model for real-time fraud detection with strict latency SLO (p99 < 100ms). They expect variable traffic with spikes. They also want to minimize cold-start latency. Which two configurations should they use? (Choose 2)

Accepted Answer

Use a GPU-enabled machine type (e.g., N1 with T4) to accelerate inference.. GPU-enabled machine types (e.g., N1 with T4) significantly accelerate TensorFlow model inference, which is critical for meeting the p99 < 100ms latency SLO. GPUs parallelize matrix operations common in deep learning models, reducing per-request latency even under variable traffic spikes.

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Set min_replicas = 0 to allow scale-to-zero and save costs.

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Enable Vertex AI Model Optimization for automatic quantization.

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Use batch prediction instead of online prediction.

Question 15

You have deployed a text classification model using Vertex AI Endpoints. The model is performing well, but the operations team wants to be alerted if the endpoint returns an excessive number of HTTP 503 errors. What is the simplest way to achieve this?

Accepted Answer

Create a Cloud Monitoring alert based on the metric 'prediction/failed_request_count' with a condition on 5xx errors.. Vertex AI Endpoints automatically export the 'prediction/failed_request_count' metric to Cloud Monitoring, which includes a label for HTTP status codes. By creating an alert on this metric with a filter for 5xx errors, you can directly monitor excessive 503 responses without additional infrastructure or custom code.

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Configure a Cloud Monitoring uptime check on the endpoint URL.

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Add a logging statement in the custom prediction routine to count errors manually.

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Export Cloud Logging to BigQuery and run a scheduled query for 503s.

Question 16

You have a Vertex AI endpoint with two deployed models: model A (champion) and model B (challenger). Traffic split is 90:10. You want to gradually increase model B's traffic to 50% over a week. What is the best way to update the traffic split?

Accepted Answer

Use the gcloud ai endpoints update command to change traffic split.. The `gcloud ai endpoints update` command allows you to directly modify the traffic split of an existing Vertex AI endpoint without redeploying or recreating the endpoint. This is the intended method for gradually shifting traffic between deployed models, as it supports incremental updates to the `--traffic-split` parameter, enabling a controlled rollout from 90:10 to 50:50 over a week.

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Use gcloud ai models upload to overwrite model B with new settings.

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Create a new endpoint and migrate traffic gradually using a load balancer.

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Delete model B and redeploy with a new traffic split.

Question 17

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. 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.

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

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Enable cross-region query in BigQuery settings

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Export the model from US and import it to EU

Question 18

Refer to the exhibit. A data analyst creates a BigQuery ML logistic regression model for churn prediction. The model evaluation shows high precision but low recall. Which change to the model creation would most likely improve recall?

Accepted Answer

Use ML.ADJUST_THRESHOLD to lower the classification threshold.. Lowering the classification threshold (e.g., from 0.5 to 0.3) will classify more customers as positive (churn), increasing recall (true positives / (true positives + false negatives)). In BigQuery ML, ML.ADJUST_THRESHOLD directly modifies the decision boundary, trading off precision for recall. This is the most direct way to address low recall without altering the model architecture or training data.

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Drop more columns to reduce overfitting.

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Increase the training data by including customers without churn dates.

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Change model_type to 'BOOSTED_TREE_CLASSIFIER'.

Question 19

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.

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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 20

Drag and drop the steps to set up a batch prediction job using Vertex AI in the correct order.

Accepted Answer

Prepare input data, then register model, then create batch prediction job, then submit job, then retrieve results.. The correct sequence for setting up a batch prediction job using Vertex AI is to first prepare your input data, then register your model, create the batch prediction job, submit the job, and finally retrieve the results. This order ensures that all necessary resources are available before job creation and submission, and that results are only requested after job completion.

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Register model, then prepare input data, then create batch prediction job, then submit job, then retrieve results.

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Create batch prediction job, then prepare input data, then register model, then submit job, then retrieve results.

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Prepare input data, then register model, then create batch prediction job, then retrieve results, then submit job.