PMLE Practice Test 16 — 15 Questions

Question 1

A data scientist trained a model on historical data from 2020-2022 and deployed it in January 2023. In February 2023, the model's accuracy drops significantly. Which monitoring metric would most likely indicate the root cause?

Accepted Answer

Training-serving skew detected by Vertex AI Model Monitoring.. Option D is correct because Vertex AI Model Monitoring specifically detects training-serving skew, which occurs when the distribution of input features at serving time differs from the training data distribution. Since the model was trained on 2020-2022 data and deployed in January 2023, a significant accuracy drop in February 2023 likely indicates that the real-world data distribution has shifted (e.g., seasonal patterns, new user behavior), causing the model to encounter unseen patterns. This skew is a common root cause of performance degradation and is directly monitored by Vertex AI's skew detection feature.

Answer

Number of unique users calling the endpoint.

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Prediction latency p99.

Answer

Number of missing feature values in requests.

Question 2

A data-processing pipeline using Dataflow needs to incorporate a custom ML prediction step. The team wants to maintain fast processing and minimize latency. What is the optimal approach?

Accepted Answer

Use a custom ParDo transform in Dataflow that calls Vertex AI Prediction API directly. Option B is correct because using a custom ParDo transform in Dataflow allows the pipeline to call the Vertex AI Prediction API synchronously within each worker, avoiding the overhead of external triggers, intermediate storage, or asynchronous messaging. This keeps the data in-memory and minimizes latency by processing predictions inline with the Dataflow streaming or batch pipeline.

Answer

Write the data to Cloud Storage, trigger a Cloud Function to call the model, and write results back

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Send data to a Pub/Sub topic and have a separate subscriber that runs predictions

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Stream data through Cloud Functions that serve predictions and write to BigQuery

Question 3

A financial institution uses a machine learning model to approve loans. They must monitor for fairness and bias. Which THREE Google Cloud tools or features can help them achieve this? (Choose 3.)

Accepted Answer

What-If Tool. The What-If Tool (WIT) is a Google Cloud tool integrated with Vertex AI that allows users to analyze model behavior across different subsets of data, such as demographic groups. It provides interactive visualizations to test how changes in input features affect predictions, enabling fairness assessments by comparing performance metrics across groups. This directly supports monitoring for bias in loan approval decisions.

Answer

Cloud Data Loss Prevention

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Cloud Healthcare API

Question 4

The exhibit shows a Cloud Composer environment variable configuration. An ML pipeline DAG fails with an authentication error when trying to access Vertex AI. What is the most likely cause?

Accepted Answer

The Airflow worker does not have the proper scopes to access Vertex AI. The authentication error when accessing Vertex AI from Cloud Composer most likely occurs because the Airflow worker's service account lacks the necessary OAuth scopes or IAM permissions. Cloud Composer uses a worker service account to execute tasks; if this account does not have the `https://www.googleapis.com/auth/cloud-platform` scope or the `aiplatform.user` role, the Airflow worker cannot authenticate to Vertex AI APIs, resulting in a 403 or 401 error.

Answer

The service account key in the environment variable is expired

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The DAG file is missing a required Python library

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The Cloud Composer environment is in a different project than Vertex AI

Question 5

The exhibit shows a Vertex AI PipelineJob submission command. The pipeline fails because the component cannot find the input data. What is the most likely cause?

Accepted Answer

The input data path is not accessible by the Vertex AI Pipelines service account. Option C is correct because the most likely cause of the pipeline failing to find input data is that the Vertex AI Pipelines service account lacks the necessary permissions to access the specified input data path. Vertex AI Pipelines uses the Compute Engine default service account (or a custom service account) to read data from Cloud Storage or other sources; if this account does not have the `storage.objectViewer` role (or equivalent) on the bucket or object, the component will fail with a permission-denied error, even if the path is syntactically correct.

Answer

The pipeline root path is incorrect

Answer

The pipeline name is misspelled

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The region does not support the component

Question 6

Which THREE are best practices for implementing CI/CD for ML pipelines on Google Cloud? (Choose THREE.)

Accepted Answer

Maintain separate environments for dev, staging, and production. Maintaining separate environments for dev, staging, and production is a core CI/CD best practice because it isolates changes, prevents accidental breakage in production, and allows thorough validation at each stage. On Google Cloud, this aligns with using distinct Vertex AI Pipelines instances or separate projects to enforce environment-specific configurations and access controls.

Answer

Design pipelines with low-code components to reduce development time

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Write unit tests for every training job

Question 7

A company uses Vertex AI Pipelines with Kubeflow DSL for hyperparameter tuning. They notice that some trials fail due to OOM errors. How should they configure the pipeline to automatically handle this?

Accepted Answer

Add a retry policy to the hyperparameter tuning step with backoff. Option D is correct because Vertex AI Pipelines supports retry policies on individual pipeline steps, including hyperparameter tuning jobs. By adding a retry policy with exponential backoff, the pipeline can automatically re-run failed trials caused by transient OOM errors without manual intervention, while avoiding immediate retries that could overload resources.

Answer

Use a larger machine type for the whole pipeline

Answer

Use Cloud Composer to catch failures and resubmit

Answer

Reduce the number of trials

Answer

Increase the memory for all trials in the pipeline definition

Question 8

A data scientist trains an XGBoost model on Vertex AI with a custom container. The model performs well on a held-out test set but fails to generalize in production. They suspect data leakage between training and validation. What is the best practice to prevent this?

Accepted Answer

Store and serve features using Vertex AI Feature Store with point-in-time correctness. Option A is correct because Vertex AI Feature Store with point-in-time correctness ensures that for each training example, only feature values that were known at the time of the prediction (i.e., before the label occurred) are used. This prevents future data from leaking into the training set, which is the most common cause of poor generalization when temporal ordering matters. The Feature Store automatically retrieves the latest feature value as of a specified timestamp, eliminating the need for manual joins and windowing logic.

Answer

Implement feature engineering in Vertex AI Pipelines to ensure temporal ordering

Answer

Store all features in BigQuery and join on timestamp during training and serving

Answer

Use Vertex AI AutoML instead of custom training

Question 9

A company deploys a model to Vertex AI Prediction with autoscaling enabled. During a flash sale, traffic spikes 10x, but the endpoint fails to scale fast enough, causing high latency. What is the most likely cause and solution?

Accepted Answer

The min_nodes setting is too low; increase min_nodes to handle baseline traffic. The correct answer is A. With Vertex AI Prediction autoscaling, the `min_nodes` setting defines the baseline number of instances that are always kept running. During a flash sale, traffic spikes 10x, but if `min_nodes` is set too low, the autoscaler cannot provision new instances quickly enough to handle the sudden load, resulting in high latency. Increasing `min_nodes` ensures a sufficient baseline capacity to absorb the initial spike while the autoscaler scales up additional nodes.

Answer

Switch to preemptible VMs to reduce cost and allow more instances

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The model container is too large; rebuild with a smaller image

Answer

Use Cloud Functions to pre-warm instances before the sale

Question 10

An ML engineer needs to run batch predictions on tens of petabytes of data using a trained model. The data is stored in Cloud Storage. Which service should they choose?

Accepted Answer

Vertex AI Batch Prediction. Vertex AI Batch Prediction is the correct choice because it is a managed service specifically designed for high-throughput, large-scale batch inference on data stored in Cloud Storage. It automatically handles sharding, scaling, and resource management for tens of petabytes, without requiring the engineer to manage infrastructure or write custom distributed processing code.

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Cloud Dataflow with the model as a side input

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Cloud Dataproc running Spark ML

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Cloud Run with multiple revisions

Question 11

A company has a large-scale ML system that uses Vertex AI Pipelines to retrain models weekly. The pipeline includes a custom training job and a batch prediction step. After moving to production, they observe that batch prediction jobs often fail with 'Quota exceeded' errors. The project has sufficient CPU quota. What is the most likely cause?

Accepted Answer

The batch prediction job is requesting a specific accelerator type that has a separate quota limit.. The most likely cause is that the batch prediction job is requesting a specific accelerator type (e.g., GPU or TPU) that has a separate quota limit from CPU quota. In Vertex AI, accelerator quotas are distinct from general compute (CPU) quotas, and even if the project has sufficient CPU quota, the accelerator quota may be exhausted, causing 'Quota exceeded' errors.

Answer

The pipeline is exceeding the maximum number of concurrent pipeline runs.

Answer

The batch prediction job is using a machine type that is not available in the region.

Answer

The custom training job is consuming all available quota before the batch prediction job starts.

Question 12

A company has a TensorFlow model that requires GPU for inference. They are deploying on Vertex AI. Which TWO configurations are necessary to ensure GPU is used?

Accepted Answer

Select a machine type that includes a GPU (e.g., NVIDIA Tesla T4).. Option D is correct because Vertex AI requires you to explicitly select a machine type that includes a GPU (e.g., n1-standard-4 with an attached NVIDIA Tesla T4) to provide the physical hardware for GPU acceleration. Without selecting a GPU machine type, the inference will run on CPU only, regardless of any other configuration.

Answer

Set the environment variable TF_GPU_ALLOCATOR=cuda_malloc_async.

Answer

Use the pre-built TensorFlow serving container, which automatically uses GPU if available.

Answer

Build a custom container with GPU drivers.

Question 13

Which TWO practices are important when scaling a prototype ML model to production on Google Cloud? (Choose two.)

Accepted Answer

Set up model monitoring for data drift and concept drift. Option A is correct because model monitoring for data drift and concept drift is essential in production ML on Google Cloud. Services like Vertex AI Model Monitoring automatically track feature distributions and prediction quality over time, alerting when the statistical properties of incoming data deviate from the training baseline. Without this, a model's accuracy can silently degrade as real-world data shifts, leading to poor business decisions.

Answer

Manually engineer features for each training iteration

Answer

Run the model on a single high-memory Compute Engine VM

Answer

Use proprietary libraries to maximize performance regardless of lock-in

Question 14

A company has a TensorFlow model that uses custom operations compiled as .so files. They want to deploy it on Vertex AI for online predictions. The model runs correctly when loaded locally. However, on Vertex AI, the prediction fails with a 'Op type not registered' error. What is the most likely reason?

Accepted Answer

The custom ops were compiled for a different CPU architecture.. Option D is correct because custom TensorFlow operations compiled as .so files are architecture-specific. If the local machine uses a different CPU architecture (e.g., x86_64 with AVX2) than the Vertex AI serving nodes (e.g., x86_64 without AVX2 or ARM), the dynamic library will fail to load, causing the 'Op type not registered' error. The model runs locally because the ops are available, but on Vertex AI the shared object cannot be loaded, so TensorFlow cannot register the custom kernels.

Answer

The model is using a deprecated TensorFlow version.

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The custom ops are not included in the model directory.

Answer

The prediction request format is incorrect.

Question 15

A company is using AutoML Tables to build a fraud detection model. The dataset has 10 million rows with 100 features, heavily imbalanced (fraud cases 0.1%). They used AutoML Tables with default settings and achieved high precision but very low recall. They need to deploy the model for real-time scoring on a Vertex AI Endpoint. The model will be used by a transaction processing system that requires low latency (<100 ms per prediction) and high throughput. The team is concerned about cost as the endpoint will receive up to 5,000 predictions per second. After deploying the model, they notice that the endpoint's latency occasionally spikes to over 1 second during peak hours. The team wants to optimize both model performance (recall) and serving performance. Which course of action should they take?

Accepted Answer

Retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled.. Option A is correct because AutoML Tables allows adjusting class weights to handle imbalanced datasets, which directly addresses the low recall issue by penalizing misclassifications of the minority class more heavily. Deploying on Vertex AI Prediction with autoscaling ensures the endpoint can handle up to 5,000 predictions per second while maintaining low latency, as autoscaling dynamically adjusts resources based on traffic, preventing spikes during peak hours.

Answer

Use BigQuery ML to create a logistic regression model with class weights, then deploy it on Cloud Run with maximum concurrency.

Answer

Export the AutoML Tables model as a TensorFlow SavedModel and deploy it on Vertex AI Prediction with a larger machine type and increased min replicas.

Answer

Use Vertex AI Workbench to manually tune a deep neural network with class imbalance techniques, then deploy as a custom container on App Engine.