PMLE Practice Test 27 — 15 Questions

Question 1

A machine learning team uses Vertex AI Pipelines to orchestrate their training pipeline. They want to trigger the pipeline automatically in response to new data arriving in a Cloud Storage bucket, and also support a scheduled run every day at 6 AM. Which combination of services should they use to achieve both event-driven and schedule-based triggers?

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

Accepted Answer

Cloud Scheduler for the schedule, and Cloud Functions triggered by Cloud Storage events to call the Vertex AI API for event-driven.. Cloud Scheduler can trigger the pipeline at 6 AM daily via a cron job, while Cloud Functions, triggered by Cloud Storage events (e.g., object finalize), can call the Vertex AI API to start the pipeline when new data arrives. This combination provides both schedule-based and event-driven triggers without requiring custom infrastructure.

Answer

Cloud Scheduler for the schedule, and Cloud Pub/Sub with Push subscription to Vertex AI for event-driven.

Answer

Cloud Functions for both schedule and event-driven, using cron trigger.

Answer

Vertex AI Pipelines built-in scheduler for schedule, and Cloud Pub/Sub for event-driven.

Question 2

A company has deployed a model that predicts customer churn. The model's performance, as measured by AUC, has been declining over the past month. The team suspects data drift. They have enabled Vertex AI Model Monitoring, but no alerts have been triggered. What is a possible reason for the lack of alerts?

Accepted Answer

The monitoring is only sampling 10% of the serving data. If the sampling rate is low (e.g., 10% of serving data), Model Monitoring may not capture enough data to detect drift, leading to no alerts even if drift exists. A low threshold would create more alerts, not fewer. Daily retraining might correct drift, but would still likely trigger alerts if drift occurred between retraining runs. Restricting to categorical features only would miss continuous feature drift, but that would still trigger alerts for categorical features.

Answer

The drift detection threshold is set too low

Answer

The model is being retrained daily

Answer

The drift detection focuses on categorical features only

Question 3

A company wants to set up end-to-end monitoring for a Vertex AI model. Which three components should they include?

Accepted Answer

Model performance metrics. Model performance metrics (Option B) are essential for end-to-end monitoring because they track how well the Vertex AI model is performing over time using key indicators like accuracy, precision, recall, or AUC-ROC. This allows the team to detect degradation in prediction quality, which is a core requirement for maintaining model reliability in production.

Answer

Feature store backup status

Answer

Model training cost

Question 4

Drag and drop the steps to perform a hyperparameter tuning job on Vertex AI in the correct order.

Accepted Answer

Step 1: Define the search space. Step 2: Create and run the tuning job. Step 3: Monitor the job. Step 4: Select the best parameters.. Define the search space, then create and run the tuning job, monitor, and select the best parameters.

Answer

Step 1: Create and run the tuning job. Step 2: Define the search space. Step 3: Monitor the job. Step 4: Select the best parameters.

Answer

Step 1: Define the search space. Step 2: Create and run the tuning job. Step 3: Select the best parameters. Step 4: Monitor the job.

Answer

Step 1: Monitor the job. Step 2: Define the search space. Step 3: Create and run the tuning job. Step 4: Select the best parameters.

Question 5

What is the most likely cause of the error?

Accepted Answer

The data split column contains only NULL values, so no rows are assigned to the training set. When the data split column contains only NULL values, BigQuery ML cannot assign any rows to the training set. The `DATA_SPLIT_METHOD` using a custom column requires non-NULL values in that column to partition data into training and evaluation sets; if all values are NULL, the training set receives zero rows, causing the model creation to fail with an error about insufficient training data.

Answer

The model type 'linear_reg' is incompatible with the column 'price' because of missing values

Answer

The model creation does not have permission to read the dataset in BigQuery

Answer

The model creation did not specify a training budget, so default is insufficient

Question 6

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.

Answer

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

Answer

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

Answer

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

Question 7

Your organization has a large production system that uses Vertex AI Prediction for an NLP model with a 2 GB memory footprint. The endpoint is configured with 5 replicas, each using an n1-standard-4 with a single T4 GPU. Recently, you observed an increase in 503 errors during peak hours. Cloud Monitoring shows that GPU utilization is consistently above 90% across all replicas, while CPU and memory are below 50%. You have already increased the max replicas to 10, but the errors persist because the increased replicas also become saturated. What should you do to resolve the issue?

Accepted Answer

Switch to a larger GPU such as V100 or A100 to increase per-replica throughput.. GPU utilization is above 90% across all replicas, indicating the GPU is the bottleneck. Increasing replicas does not help because each new replica also saturates its GPU. The most direct solution is to increase per-replica throughput by using a more powerful GPU (e.g., V100 or A100), which can process more work per unit time. Option A addresses the root cause—GPU capacity. Option D (high-memory machine) does not help because memory is not the constraint (CPU/memory < 50%). Batching (B) is likely already in use but limited by GPU compute capacity. Model parallelism (C) is unnecessary for a 2 GB model.

Answer

Implement request batching in the custom container to improve GPU utilization efficiency.

Answer

Enable model parallelism across multiple GPUs within each replica.

Answer

Use a high-memory machine type like n1-highmem-16 to reduce memory pressure.

Question 8

A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?

Accepted Answer

Enable point-in-time correct retrieval when creating training datasets. Using the same feature view for training and serving ensures consistency. Point-in-time correct retrieval prevents leakage. Feature monitoring detects drift that could indicate skew.

Answer

Use different feature views for training and serving to compare performance

Answer

Export training data from the online store directly

Question 9

Which TWO of the following are benefits of using Vertex AI Matching Engine (Vector Search) over a brute-force nearest neighbor search? (Choose 2)

Accepted Answer

Lower query latency for large datasets. Vertex AI Matching Engine uses approximate nearest neighbor (ANN) algorithms like ScaNN (Scalable Nearest Neighbors) to index high-dimensional vectors. For large datasets, ANN dramatically reduces query latency by avoiding a full scan of all vectors, unlike brute-force search which must compute distances against every vector. This makes option C correct because ANN trades a negligible accuracy loss for orders-of-magnitude faster retrieval.

Answer

Simpler to implement than brute-force

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Supports real-time updates without index rebuild

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Guaranteed exact nearest neighbor results

Question 10

A team deploys a model using Vertex AI and wants to monitor for concept drift. What should they track?

Accepted Answer

Changes in the relationship between inputs and outputs. Concept drift refers to a change in the underlying relationship between the input features and the target variable over time, which degrades model performance. In Vertex AI, monitoring this requires tracking the statistical relationship between inputs and outputs (e.g., via prediction residuals or model performance metrics), not just the input distribution alone. Option D correctly identifies this need, as concept drift is fundamentally about the input-output mapping shifting, even if the input distribution remains stable.

Answer

Number of prediction requests

Answer

Model prediction latency

Answer

Changes in input data distribution

Question 11

A company uses Vertex AI Pipelines to train models on a daily schedule. The pipeline includes a component that runs a BigQuery query to extract features. The team wants to ensure that if the BigQuery component fails due to transient network errors, the pipeline automatically retries it. How can they configure retries in Vertex AI Pipelines?

Accepted Answer

Set the `retry` parameter of the component to a positive integer, for example `retry=3`.. Vertex AI Pipelines natively supports a `retry` parameter on pipeline components. Setting `retry=3` instructs the pipeline to automatically retry the component up to three times if it fails due to transient errors, such as network timeouts. This is the simplest and most direct way to handle retries within the Vertex AI Pipelines orchestration framework.

Answer

Deploy the component as a Cloud Function and configure Cloud Functions retry.

Answer

Wrap the component in a `dsl.If` conditional that checks for failure and re-submits the component.

Answer

Use Cloud Composer with a task retry policy in Airflow.

Question 12

Which TWO actions are recommended for collaborating on machine learning models using Vertex AI Model Registry?

Accepted Answer

Use version aliases such as 'champion' and 'challenger' to manage model lifecycle.. Vertex AI Model Registry supports version aliases like 'champion' and 'challenger' to designate which model version should serve as the production candidate and which is under evaluation, enabling controlled lifecycle management and A/B testing without manual version tracking.

Answer

Use Cloud Storage object labels to store model descriptions.

Answer

Deploy all model versions to a single endpoint for comparison.

Answer

Create a separate model entry for each training run.

Question 13

An ML engineer is using Cloud Build to trigger a Vertex AI Pipeline on every commit to a repository. The pipeline takes 2 hours. The engineer wants to only run the pipeline when changes are made to specific directories. How can this be achieved?

Accepted Answer

Configure Cloud Build trigger with included file globs. Cloud Build triggers support 'included file globs' and 'ignored file globs' to filter which file changes should invoke the trigger. By specifying glob patterns for the directories of interest, the trigger will only fire when commits modify files matching those patterns, avoiding unnecessary pipeline runs for unrelated changes.

Answer

Use Cloud Composer to poll the repository periodically

Answer

Use a Cloud Function to evaluate changes and invoke the pipeline

Answer

Modify the pipeline to ignore unrelated changes

Answer

Add a conditional step in the pipeline to abort if no relevant changes

Question 14

An ML engineer is designing a CI/CD pipeline for ML models using Cloud Build and Cloud Deploy. They want to automatically test model performance on a validation set before promoting to production. Which step should be included in the CI/CD pipeline?

Accepted Answer

Run a Vertex AI Pipeline for model evaluation and register the model only if metrics exceed thresholds. It directly integrates model evaluation into the CI/CD pipeline using Vertex AI Pipelines, which allows automated validation of model performance against predefined thresholds before promotion. This ensures that only models meeting quality criteria are deployed, aligning with MLOps best practices for gated promotions.

Answer

Run unit tests on the training code

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Use Cloud Composer to schedule evaluation

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Deploy to production immediately after training

Answer

Train the model in the CI/CD pipeline

Question 15

Refer to the exhibit. A Vertex AI prediction endpoint is failing with a deadline exceeded error. The log shows the following. What is the most likely cause?

Accepted Answer

Insufficient CPU or memory for the load. A deadline exceeded error in Vertex AI prediction endpoints typically indicates that the model is taking too long to respond, often due to insufficient CPU or memory resources for the current load. This causes the request to time out before the inference completes, as the underlying infrastructure cannot process the requests quickly enough.

Answer

The prediction request is malformed

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The model is too large for the machine type

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The model version is corrupted