PMLE Practice Exam 53 — 20 Questions

Question 1

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

Answer

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

Question 2

A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?

Accepted Answer

Deploy the model to a Vertex AI Endpoint directly from Model Garden.. Deploying directly to a Vertex AI Endpoint from Model Garden eliminates all infrastructure management. Vertex AI handles model serving, scaling, and monitoring automatically, which is ideal for a team with no experience in model serving infrastructure. This is a fully managed, serverless deployment that requires no containerization or server configuration.

Answer

Deploy the model as a Cloud Function invoked by Cloud Storage events.

Answer

Deploy the model as a Cloud Run service using a custom Docker container.

Answer

Deploy the model on App Engine flexible environment.

Question 3

Which TWO strategies help ensure data consistency when multiple teams are contributing features to a shared Vertex AI Feature Store?

Accepted Answer

Define and enforce feature schemas using the Feature Store API.. Defining and enforcing feature schemas using the Vertex AI Feature Store API ensures that all teams adhere to a consistent data structure (e.g., fixed feature names, data types, and value ranges). This prevents schema drift and ingestion conflicts, which are common when multiple teams independently push features to the same feature store. Without schema enforcement, one team might inadvertently change a feature's data type or add unexpected values, breaking downstream models.

Answer

Each team should create their own feature store to avoid conflicts.

Answer

Use only batch ingestion to keep features synchronized.

Answer

Allow each team to independently define feature engineering logic.

Question 4

Refer to the exhibit. What is the purpose of this query?

Accepted Answer

To find prediction errors in Cloud Logging. The query filters Cloud Logging entries for the string 'prediction failed', which directly indicates prediction errors logged by the ML prediction service. This is a common pattern for monitoring model inference failures in production, not for measuring drift, counting requests, or measuring latency.

Answer

To detect data drift

Answer

To count all prediction requests

Answer

To monitor model latency

Question 5

You are troubleshooting a failed Vertex AI AutoML training pipeline. The error log shows: 'ValueError: budget_milli_node_hours must be greater than 0'. What is the root cause?

Accepted Answer

The budget_milli_node_hours parameter is set to 0, which is below the minimum required value. In Vertex AI AutoML training, the `budget_milli_node_hours` parameter specifies the maximum compute time in milliseconds. Setting it to 0 means no compute time is allocated, which is below the minimum required value (typically 1 or higher depending on task type). This causes an immediate validation failure, preventing the training job from starting. Options B, C, and D describe issues that are not the root cause given the correct answer.

Answer

The evaluate_model component expects the model artifact but the autopilot_train component does not output a model artifact

Answer

The location parameter 'us-central1' is not a valid region for AutoML

Answer

The threshold parameter is missing in the autopilot_train component

Question 6

You need to deploy a model to a Vertex AI endpoint that can scale down to zero when there are no requests to minimize costs. Which feature should you enable?

Accepted Answer

Enable autoscaling with minReplicaCount=0. Vertex AI endpoints support autoscaling with a `minReplicaCount` of 0, which allows the endpoint to scale down to zero instances when there are no incoming requests, thereby minimizing costs. This feature is specifically designed for serverless model serving, where the endpoint automatically scales up from zero when traffic arrives and scales down to zero during idle periods.

Answer

Deploy the model to a Compute Engine instance and use instance groups.

Answer

Use a custom metric for autoscaling

Answer

Set maxReplicaCount to 0

Question 7

An ML team is fine-tuning a large language model using a custom container on Vertex AI. They want to reduce costs by using preemptible (spot) VMs for training. The training job is long-running and uses checkpointing. Which statement is correct regarding spot VM usage?

Accepted Answer

You must enable checkpointing in the training code and use spot VMs by setting the 'spot' field in the machine spec. Vertex AI custom training jobs support spot VMs, but you must explicitly enable checkpointing in your training code and set the 'spot' field in the machine spec to true. This ensures that when a preemptible VM is terminated, the training can resume from the latest checkpoint, preventing loss of progress and reducing costs.

Answer

Spot VMs are not available for custom training jobs on Vertex AI

Answer

Training will automatically resume from the latest checkpoint without any configuration

Answer

Spot VMs cannot be used with GPU accelerators

Question 8

A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?

Accepted Answer

Vertex AI Feature Store. Vertex AI Feature Store is purpose-built for managing and sharing ML features, with an online store for low-latency serving. BigQuery is for analytics, Cloud Storage for objects, and Vertex AI Model Registry for models.

Answer

Vertex AI Model Registry

Answer

Cloud Storage

Answer

BigQuery

Question 9

You need to set up monitoring for a Vertex AI model that serves predictions in real-time. The model is expected to have a latency SLA of under 100ms. Which metric should you configure an alert on to ensure the SLA is met?

Accepted Answer

p99 latency of prediction requests. P99 latency measures the worst-case latency experienced by 99% of requests, which is the standard metric for enforcing a strict SLA like under 100ms. Monitoring p99 ensures that even the slowest 1% of requests do not violate the threshold, providing a robust guarantee for real-time predictions.

Answer

p50 latency of prediction requests

Answer

Prediction drift score

Answer

Number of prediction requests per second

Question 10

A company needs to build a custom model to classify images of products into categories. They have a large labeled dataset. They want to use AutoML but are unsure which options support image classification. Which TWO AutoML products support image classification?

Accepted Answer

AutoML Video. Both AutoML Vision and AutoML Video Intelligence support image classification. AutoML Vision (Option C) is designed for static image classification, object detection, and segmentation. AutoML Video Intelligence (Option B) can classify objects and actions in video frames, effectively supporting image classification on individual frames. Therefore, both B and C are correct.

Answer

AutoML Natural Language

Answer

AutoML Translation

Answer

AutoML Tables

Question 11

You are a machine learning engineer at a retail company. You have deployed a product recommendation model on Vertex AI Prediction using a custom container. The model is a TensorFlow SavedModel that computes embeddings using a large lookup table. The endpoint is configured with 2 replicas on n1-standard-4 (4 vCPU, 15 GB memory) machines. After deployment, you notice that the endpoint's memory usage grows over time, eventually reaching 90% and causing requests to fail with 503 errors. The container logs show no errors, but the memory usage graph shows a steady increase. The model loads the embedding table (5 GB) at startup. You suspect a memory leak. Which course of action should you take first to diagnose and resolve the issue?

Accepted Answer

Profile the container's memory usage locally with memory_profiler to find the leak, then fix the code.. The steady memory growth despite a fixed 5 GB embedding table indicates a memory leak in the custom container code, not a capacity issue. Profiling locally with memory_profiler allows you to trace object allocations and identify the leak source before modifying the serving code, which is the most direct diagnostic step.

Answer

Reduce the number of replicas to 1 to reduce memory contention.

Answer

Increase the machine memory to n1-standard-8 (30 GB).

Answer

Restart the endpoint every hour using a Cloud Scheduler job.

Question 12

A team of data scientists is collaborating on notebooks in Vertex AI Workbench. They need to use Git for version control and share notebooks with real-time editing. Which type of Workbench instance should they choose?

Accepted Answer

Managed notebooks. Managed notebooks in Vertex AI Workbench support real-time collaborative editing (similar to Google Docs) and can integrate with Git for version control via Cloud Source Repositories. User-managed notebooks are single-user and lack real-time collaboration, so they do not meet both requirements.

Answer

User-managed notebooks

Answer

Vertex AI Pipelines

Answer

Dataproc Jupyter notebooks

Question 13

A company needs to classify images of products into categories (e.g., electronics, clothing, food). They have labeled images and want to use a low-code solution on Google Cloud. Which service is suitable for this task?

Accepted Answer

AutoML Vision. AutoML Vision is the only low-code service among the options that allows you to train custom image classification models using labeled images without writing code. It directly matches the requirement to classify product images into categories. The other options are unsuitable: AutoML Tables is for tabular data, Cloud Vision API using product search is for finding similar products rather than custom classification, Vertex AI Workbench with a custom TensorFlow model requires custom coding, and Document AI is designed for document processing.

Answer

AutoML Tables

Answer

Cloud Vision API using product search

Answer

Vertex AI Workbench with a custom TensorFlow model

Answer

Document AI custom extractor

Question 14

An engineer is designing a distributed training job on Vertex AI for a TensorFlow model that uses the MultiWorkerMirroredStrategy. They need to ensure proper communication between workers. Which environment variable must be set correctly for each worker?

Accepted Answer

TF_CONFIG. In TensorFlow distributed training with MultiWorkerMirroredStrategy, the only required environment variable is `TF_CONFIG`. It provides the cluster topology and task identity, enabling gRPC communication between workers. The distribution strategy is defined in code, not via an environment variable. `TF_DISTRIBUTED_STRATEGY` is not a standard TensorFlow environment variable.

Answer

CLUSTER_SPEC

Answer

TF_CPP_MIN_LOG_LEVEL

Answer

TF_CONFIG_JSON

Answer

TF_DISTRIBUTED_STRATEGY

Question 15

A large e-commerce company deploys a recommendation model on Vertex AI with autoscaling enabled. During Black Friday, traffic spikes rapidly. The autoscaler adds new instances, but new instances take several minutes to become ready (cold start). As a result, many requests time out. What should they do to mitigate this issue?

Accepted Answer

Set a higher minimum number of instances to handle the expected peak.. Setting a higher minimum number of instances ensures that a baseline capacity is always running and ready to serve traffic. This pre-warms instances, eliminating the cold-start latency during rapid traffic spikes, such as Black Friday, because new instances do not need to initialize from scratch.

Answer

Use a larger machine type to reduce the number of instances needed.

Answer

Configure the autoscaler to use CPU utilization metric instead of request count.

Answer

Increase the health check grace period for new instances.

Question 16

Your ML pipeline uses Vertex AI Feature Store to serve features for online predictions. You need to monitor the freshness of features in the online store. Which approach is most effective?

Accepted Answer

Create a custom metric in Cloud Monitoring that tracks the time since last feature update, and set an alert threshold.. Cloud Monitoring custom metrics allow you to track the timestamp of the last feature update in Vertex AI Feature Store and set an alert threshold for staleness. This directly measures feature freshness, which is critical for online predictions where stale features can degrade model accuracy. Other options either measure unrelated metrics (entity count), are too slow (nightly batch), or focus on auditing rather than real-time monitoring.

Answer

Set up a Cloud Monitoring alert for feature store entity count.

Answer

Schedule a nightly BigQuery batch job to compare feature values.

Answer

Enable detailed audit logs in Feature Store and export to BigQuery.

Question 17

A data science team uses Cloud Composer to orchestrate a complex ML workflow. They need to run a Vertex AI pipeline and then a BigQuery query conditionally based on the pipeline's output. Which Airflow features should they use? (Choose two.)

Accepted Answer

VertexAIPipelineJobOperator. VertexAIPipelineJobOperator (D) runs the Vertex AI pipeline. To conditionally run a BigQuery query based on the pipeline's output, you can use a PythonOperator (E) with if-else logic to check the output and then execute the BigQuery query within the Python task, for example by using the BigQuery client library. This avoids the need for a separate branching operator. Therefore, the correct features are D and E.

Answer

BigQueryExecuteQueryOperator

Answer

Task dependencies using >>

Answer

dsl.If

Question 18

A company uses Cloud Composer to orchestrate an ML pipeline. They notice that the pipeline occasionally fails because the Composer environment runs out of disk space on the worker nodes. The pipeline uses many large dependencies. What is the most effective long-term solution?

Accepted Answer

Mount a Cloud Storage bucket to the Composer workers using GCSFuse to store large artifacts externally.. Mounting a Cloud Storage bucket via GCSFuse allows Composer workers to access large artifacts stored externally without consuming local disk space. This provides a scalable, durable, and cost-effective solution for handling large dependencies, as the pipeline can read/write directly to Cloud Storage, eliminating the disk space bottleneck on worker nodes.

Answer

Move the pipeline to Cloud Functions to avoid Composer's disk limitations.

Answer

Reduce the size of the Docker image used by the pipeline.

Answer

Increase the number of worker nodes in the Composer environment.

Question 19

A machine learning engineer needs to deploy a model to an endpoint for real-time predictions. The model is registered in Vertex AI Model Registry. Which command should they use to create an endpoint and deploy the model with the alias 'champion'?

Accepted Answer

gcloud ai endpoints deploy-model --endpoint=my-endpoint --model=my-model --alias=champion. The correct command uses `gcloud ai endpoints deploy-model` with the model's resource name and alias.

Answer

gcloud ai models upload --model=my-model --alias=champion --endpoint=my-endpoint

Answer

gcloud ai endpoints predict --model=my-model --alias=champion

Answer

gcloud ai endpoints create --model=my-model --alias=champion

Question 20

A team is using Delta Lake on Dataproc for their data lake with ACID transactions. They want to version data for ML experiments and roll back to a previous version if needed. Which Delta Lake feature should they use?

Accepted Answer

Delta Lake time travel. Delta Lake provides time travel via VERSION AS OF or TIMESTAMP AS OF.

Answer

Delta Lake streaming

Answer

Delta Lake schema enforcement

Answer

Delta Lake optimization (Z-order)