PMLE Practice Test 19 — 15 Questions

Question 1

A team deploys a model on Vertex AI that uses a custom prediction routine (CPR) with a dependency on a native library. The container crashes with 'ImportError: libcudart.so.11.0: cannot open shared object file'. How should they resolve this?

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

Accepted Answer

Build a custom container image that includes the CUDA runtime library.. The error 'ImportError: libcudart.so.11.0: cannot open shared object file' indicates that the CUDA runtime library (version 11.0) is missing from the container environment. Since the custom prediction routine (CPR) depends on a native library that requires this CUDA runtime, the correct solution is to build a custom container image that includes the CUDA runtime library. This ensures the shared object is available at runtime, resolving the import error.

Answer

Submit the model for batch prediction to avoid the error.

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Request a GPU machine type for the endpoint.

Answer

Use a Vertex AI pre-built container for PyTorch instead.

Question 2

Drag and drop the steps to set up model monitoring for drift detection on Vertex AI in the correct order.

Accepted Answer

Deploy model, then Enable monitoring, then Set thresholds, then Configure alerts, then Review. The correct order to set up model monitoring for drift detection on Vertex AI is: 1. Deploy model to an endpoint. 2. Enable monitoring on the deployed endpoint. 3. Set drift detection thresholds (e.g., for feature distributions or prediction outputs). 4. Configure alerts (e.g., email notifications). 5. Review monitoring results and alerts.

Answer

Deploy model, then Set thresholds, then Enable monitoring, then Configure alerts, then Review

Answer

Enable monitoring, then Deploy model, then Set thresholds, then Configure alerts, then Review

Answer

Deploy model, then Enable monitoring, then Configure alerts, then Set thresholds, then Review

Question 3

Refer to the exhibit. The team wants to automatically deploy the best-performing model version to production. They have set up a Cloud Function triggered by Model Registry events. Which alias should they use in the function to get the latest champion?

Accepted Answer

'champion'. The 'champion' alias is specifically reserved in Vertex AI Model Registry to denote the best-performing model version in production. By configuring the Cloud Function to trigger on the assignment of the 'champion' alias, the team ensures that only the model version promoted as the production champion is automatically deployed, aligning with MLOps best practices for staged model promotion.

Answer

''

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'experiment'

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'latest'

Question 4

A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)

Accepted Answer

Deploy the model on a GPU-enabled machine. Option B is correct because deploying the model on a GPU-enabled machine type gives the inference workload hardware acceleration, which speeds up the matrix/tensor computations typical of ML models and thereby reduces per-request prediction latency. Option C is correct because enabling model quantization via Vertex AI Model Optimization reduces the numerical precision of weights and activations (for example to INT8), shrinking model size and memory bandwidth requirements and enabling faster inference, which lowers latency. Option A is not correct because increasing the maximum number of replicas improves throughput and availability under load, but it does not reduce the latency of an individual prediction. Option D is not correct because using a smaller machine type with less memory would likely increase latency or cause out-of-memory failures rather than reduce it. Option E is not correct because autoscaling with a lower target CPU utilization only triggers scaling sooner to add capacity; it addresses throughput/load handling, not the intrinsic latency of a single inference.

Answer

Increase the maximum number of replicas

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Use a smaller machine type with less memory

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Enable autoscaling with a lower target CPU utilization

Question 5

Refer to the exhibit. A data scientist notices that predictions from a deployed model are taking longer than expected. Which Cloud Monitoring metric should be inspected first to identify the bottleneck?

Accepted Answer

Vertex AI - Endpoint - Prediction latency distribution. The data scientist is investigating slow predictions from a deployed model. The most direct metric to identify the latency bottleneck is the prediction latency distribution, which shows the distribution of response times for online prediction requests. This metric allows you to pinpoint whether the delay is due to model inference time, network overhead, or endpoint queuing, making it the first logical place to inspect.

Answer

Vertex AI - Model - Compute utilization

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Vertex AI - Endpoint - Traffic

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Vertex AI - Endpoint - Online prediction errors

Question 6

A data analyst wants to create a classification model directly in BigQuery using SQL. Which feature should they use?

Accepted Answer

BigQuery ML. BigQuery ML (BQML) enables users to create and execute machine learning models directly in BigQuery using standard SQL syntax, without needing to export data or manage separate ML infrastructure. For a data analyst who wants to build a classification model entirely within BigQuery, BQML provides the CREATE MODEL statement with classification algorithms like logistic regression or XGBoost, making it the correct and most direct feature.

Answer

Vertex AI

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Dataflow

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Cloud ML Engine

Question 7

A company is serving a model for their e-commerce website. They expect traffic to be low at night and very high during flash sales. They want to minimize costs while ensuring availability during spikes. Which autoscaling configuration should they use?

Accepted Answer

min_replica_count=1, max_replica_count=20, target_cpu=60. Setting a high max_replica_count allows scaling to handle spikes, while a low min_replica_count saves cost during low traffic. CPU utilization target of 60% is reasonable.

Answer

min_replica_count=5, max_replica_count=5, target_cpu=60

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min_replica_count=10, max_replica_count=10, target_cpu=60

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min_replica_count=0, max_replica_count=100, target_cpu=80

Question 8

Which of the following is a benefit of using Vertex AI Endpoints with autoscaling and scale-to-zero?

Accepted Answer

It reduces costs by scaling down to zero replicas when no requests are received.. Vertex AI Endpoints with autoscaling and scale-to-zero allow the number of serving replicas to dynamically adjust based on incoming traffic. When no requests are received, the endpoint can scale down to zero replicas, meaning you are not charged for idle compute resources. This directly reduces operational costs compared to maintaining a minimum number of always-on instances.

Answer

It eliminates the need for a load balancer.

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It reduces model training time.

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It automatically upgrades the model version.

Question 9

A team is operationalizing a machine learning pipeline using Vertex AI. They want to automatically track experiment runs, log model parameters and metrics, and store model artifacts for reproducibility. They also need to capture lineage between pipeline components (e.g., which dataset and hyperparameter tuning job produced a model). Which TWO services should they use together to achieve this? (Choose two.)

Accepted Answer

Vertex AI Metadata. Vertex AI Experiments (D) is correct because it automatically tracks and compares experiment runs, logging parameters, metrics, and artifacts so results are reproducible and comparable across training runs. Vertex AI Metadata (C) is correct because it records lineage and context for ML artifacts, capturing relationships such as which dataset and hyperparameter tuning job produced a given model, which is exactly the lineage requirement. Together they cover both experiment tracking and artifact lineage. Vertex AI Model Registry (A) manages model versions and deployment lifecycle but does not itself track experiment runs or component lineage. Vertex AI Feature Store (B) serves and manages feature values for training/serving, not experiment tracking or lineage. Vertex AI Workbench (E) is a notebook development environment and does not provide the required automatic tracking or lineage services.

Answer

Vertex AI Model Registry

Answer

Vertex AI Feature Store

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Vertex AI Workbench

Question 10

Refer to the exhibit. A team deploys a model using Cloud Run. They notice that after scaling up, the new instances take about 90 seconds to become ready and serve requests. They want to reduce this startup time. Which configuration change is most likely to help?

Accepted Answer

Change the container image to use a smaller base image. Changing the container image to use a smaller base image reduces the image size and the number of layers that must be pulled and unpacked, which directly reduces container startup time on new Cloud Run instances. A smaller image means less data to download and less filesystem work before the application can start, addressing the 90-second startup delay. This is the most likely configuration change to improve cold start performance.

Answer

Reduce the startupProbe initialDelaySeconds to 30

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Reduce the memory limit to 4Gi

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Increase the containerConcurrency to 100

Question 11

A model deployed on Vertex AI Prediction is returning high latency for real-time requests. The model is a small TensorFlow model. Which troubleshooting step should the team take first?

Accepted Answer

Check if the machine type is too small and enable autoscaling. High latency for real-time predictions from a small TensorFlow model often indicates that the serving infrastructure is under-provisioned. Checking the machine type and enabling autoscaling directly addresses whether the instance is too small to handle the request volume, which is the most common first step in diagnosing latency issues on Vertex AI Prediction.

Answer

Retrain the model with a larger batch size

Answer

Use a custom container with optimized runtime

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Enable Cloud Armor to reduce traffic

Question 12

A company wants to use Vertex AI JumpStart to deploy a pre-trained image classification model and later fine-tune it on their own data. Which TWO statements are true about Vertex AI JumpStart?

Accepted Answer

JumpStart allows you to fine-tune foundation models like Gemma. Vertex AI JumpStart supports fine-tuning of foundation models like Gemma, allowing users to adapt pre-trained models to their specific datasets. This capability is built into JumpStart's managed environment, which handles the underlying infrastructure for training and deployment.

Answer

JumpStart requires users to build custom Docker containers for all models

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JumpStart only supports text-based models

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JumpStart only supports tabular data models

Question 13

A company needs to run batch predictions on 10 TB of data stored in Cloud Storage. The predictions should be written to BigQuery. Which approach should they use?

Accepted Answer

Create a Vertex AI Batch Prediction job with GCS input and BigQuery output. Vertex AI Batch Prediction natively supports reading input from Cloud Storage and writing predictions directly to BigQuery, making it the most efficient and fully managed solution for large-scale batch inference on 10 TB of data. This approach avoids the complexity of custom infrastructure or per-record model calls, leveraging Vertex AI's optimized batch processing pipeline.

Answer

Export the model to Cloud Functions and trigger on file upload

Answer

Use Vertex AI Online Prediction with a batch job

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Use Dataflow to read from GCS and write to BigQuery, calling the model for each record

Question 14

A machine learning team is building a feature engineering pipeline using Dataflow. They need to compute features from streaming data and store them in Vertex AI Feature Store for online serving. The features must be updated within 5 seconds of the event. Which TWO services should they combine? (Select 2)

Accepted Answer

Cloud Dataflow for stream processing and feature computation. Cloud Dataflow is correct because it provides unified stream and batch processing with exactly-once semantics, enabling low-latency feature computation from streaming data. It integrates natively with Vertex AI Feature Store for online serving, ensuring features are updated within the required 5-second SLA.

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Cloud Storage for feature store

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Cloud Functions for feature transformation

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BigQuery for feature storage

Question 15

An ML engineer needs to run batch predictions on 10 TB of data stored in BigQuery using a TensorFlow model. The predictions must be written to BigQuery. Which service should they use?

Accepted Answer

Use Vertex AI Batch Prediction with BigQuery source and sink.. Vertex AI Batch Prediction natively supports BigQuery as both input source and output sink, so the engineer can submit a batch job directly against the BigQuery table and have predictions written back to BigQuery without any data movement code. This is the most efficient and least error-prone option for 10 TB of data.

Answer

Create a Dataflow pipeline to read from BigQuery, run the model using Python, and write results to BigQuery.

Answer

Export BigQuery data to GCS, run batch prediction on GCS, then load results back to BigQuery.

Answer

Use Vertex AI online prediction with batch requests.