PMLE Practice Test 30 — 15 Questions

Question 1

A team uses Vertex AI Feature Store for online serving. They notice high latency during peak hours. They have configured the feature store with Bigtable as the online serving store. What is the most likely cause of the high latency?

Accepted Answer

The online serving node count is insufficient for the QPS.. Option C is correct because Vertex AI Feature Store uses Bigtable as the online serving store, and during peak hours, high query-per-second (QPS) loads can overwhelm the serving nodes if they are under-provisioned. Insufficient node count leads to queuing and increased latency, as Bigtable's performance scales linearly with the number of nodes for read throughput. The most direct remedy is to increase the number of Bigtable nodes to match the QPS demand.

Answer

The Bigtable cluster has too many nodes.

Answer

Feature data is stored as Avro files.

Answer

Feature values are not pre-cached.

Question 2

A retail company uses Vertex AI AutoML to train a product recommendation model. They have a dataset of past purchases stored in BigQuery. The data science team wants to iteratively train and improve the model. They need to track which dataset version was used for each model and preserve the exact data for reproducibility. They currently export data to CSV files and store them in Cloud Storage. However, the dataset is updated daily, and they want to ensure that models are trained on a consistent snapshot. What should they do?

Accepted Answer

Use BigQuery snapshots to capture a versioned dataset and reference the snapshot in the training pipeline.. Option B is correct because BigQuery snapshots provide a consistent, versioned view of the dataset at a specific point in time, ensuring reproducibility without duplicating data. By referencing the snapshot in the Vertex AI training pipeline, the team can train models on the exact same data snapshot, even as the source table is updated daily. This approach avoids the overhead of exporting to CSV and Cloud Storage while maintaining data integrity and lineage.

Answer

Use Vertex AI Dataset service to create a dataset and export it to BigQuery.

Answer

Train the model directly on the BigQuery table and let AutoML handle versioning.

Answer

Export the data to a timestamped CSV file and store it in Cloud Storage before each training run.

Question 3

A data engineering team wants to orchestrate an ML pipeline that includes data preprocessing in Dataflow, AutoML training, and model deployment. They want to minimize operational overhead. Which approach is best?

Accepted Answer

Use Vertex AI Pipelines with custom components. Vertex AI Pipelines with custom components is the best choice because it provides a fully managed, serverless orchestration service that natively integrates with Dataflow, AutoML, and model deployment. This minimizes operational overhead by eliminating the need to manage infrastructure, handle retries, or maintain a separate orchestration server, while offering built-in artifact tracking and pipeline caching.

Answer

Use Cloud Composer with Apache Airflow DAG

Answer

Use AI Platform Training with script

Answer

Use Cloud Scheduler to trigger Cloud Functions

Question 4

A company is using AutoML Vision for object detection and observes high latency for online predictions. What can they do to reduce latency?

Accepted Answer

Reduce the training budget to create a smaller model. Reducing the training budget in AutoML Vision forces the model to use fewer node-hours, which typically results in a smaller and less complex model. A smaller model has fewer parameters and requires less computation during inference, directly reducing the latency for online predictions. This is a trade-off between model accuracy and inference speed.

Answer

Use continuous batch prediction instead of online prediction

Answer

Deploy the model to a region closer to the users

Answer

Use a larger batch size in the prediction request

Question 5

A data analyst wants to use low-code ML to analyze text data. Which TWO Google Cloud services are appropriate?

Accepted Answer

Cloud Natural Language API. Cloud Natural Language API is a low-code ML service that provides pre-trained models for analyzing text, including sentiment analysis, entity recognition, and syntax analysis, without requiring custom model training. It is appropriate for a data analyst who wants to quickly extract insights from text data using simple API calls.

Answer

Vertex AI Workbench

Answer

Document AI

Answer

BigQuery ML for sentiment

Question 6

A company uses Vertex AI for AutoML training. Which THREE are best practices for managing model versions?

Accepted Answer

Use Vertex AI Model Registry to version models. Vertex AI Model Registry is the central repository for managing and versioning models, allowing you to track iterations, compare performance, and control deployments. It provides a structured way to organize models, roll back to previous versions if needed, and maintain lineage for compliance and reproducibility.

Answer

Deploy each model version to a separate endpoint

Answer

Automatically delete old versions after 30 days

Question 7

A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?

Accepted Answer

Use BigQuery ML to train an ARIMA_PLUS model. Option D is correct because ARIMA_PLUS in BigQuery ML is specifically designed for time-series forecasting with multiple seasonalities (weekly and yearly). It automatically handles seasonality detection, trend decomposition, and holiday effects, making it ideal for retail sales data with clear periodic patterns.

Answer

Call a pre-built Google Cloud API for sales prediction

Answer

Use a linear regression model in Vertex AI

Answer

Use Vertex AI AutoML Tables with date as feature

Question 8

A team is architecting a low-code ML system for real-time predictions with AutoML. Which THREE considerations are critical for production?

Accepted Answer

Enable autoscaling for the endpoint. A is correct because autoscaling ensures the prediction endpoint can handle variable request loads without manual intervention, which is critical for production real-time systems. In Vertex AI, you can configure autoscaling with a target utilization level (e.g., 60%) to automatically adjust the number of compute nodes based on incoming traffic, preventing both over-provisioning and latency spikes.

Answer

Use a custom container for prediction

Answer

Use global model endpoints for low latency everywhere

Question 9

A developer sees this error when calling the endpoint. What is the most likely cause?

Accepted Answer

The model is deployed but not yet serving. The error 'model is not serving' occurs when the endpoint exists and a model is deployed, but the deployment is not yet in the 'serving' state (e.g., still loading, scaling, or warming up). In SageMaker, the endpoint must transition through 'Creating' and 'InService' before it can serve inference requests. Option B correctly identifies that the model is deployed but not yet ready to handle traffic.

Answer

The model is still in training

Answer

The endpoint has no deployed model

Answer

The request payload size exceeds limit

Question 10

A company uses Vertex AI AutoML to train a vision model, but the model has low accuracy. What should they do first?

Accepted Answer

Add more labeled images to the dataset. Adding more labeled images directly addresses the most common cause of low accuracy in AutoML vision models: insufficient or unrepresentative training data. Vertex AI AutoML relies on transfer learning from pre-trained models, and its performance is heavily dependent on the quality and quantity of labeled examples. Before adjusting hyperparameters or infrastructure, the first step should always be to improve the dataset, as AutoML is designed to handle model architecture and training budget automatically.

Answer

Switch to a custom model

Answer

Increase the training budget

Answer

Reduce image size to speed up training

Question 11

A team wants to deploy a BigQuery ML model for online prediction. Which approach should they take?

Accepted Answer

Export the model to Vertex AI and create an endpoint. BigQuery ML models can be exported directly to Vertex AI for online prediction. Vertex AI provides a managed endpoint that supports real-time serving with low latency, which is required for online prediction. Exporting to Cloud Storage and then deploying to AI Platform is outdated because AI Platform is now part of Vertex AI, and the recommended path is to export the model directly to Vertex AI and create an endpoint.

Answer

Export the model to Cloud Storage and deploy to AI Platform

Answer

None of these; BigQuery ML models cannot be used for online prediction

Answer

Use BigQuery ML's ML.PREDICT for online predictions

Question 12

A company needs to perform sentiment analysis on streaming social media data. Which architecture should they use?

Accepted Answer

Pub/Sub → Dataflow → Natural Language API → BigQuery. Option D is correct because streaming social media data requires a scalable, ordered ingestion pipeline. Pub/Sub ingests the stream, Dataflow processes it in real-time (e.g., windowing, deduplication), the Natural Language API performs sentiment analysis, and BigQuery stores results for querying. This decouples ingestion from processing and storage, enabling exactly-once semantics and auto-scaling.

Answer

Dataflow → Pub/Sub → Natural Language API → BigQuery

Answer

Pub/Sub → Cloud Functions → Natural Language API → Cloud Storage

Answer

Cloud Functions → Pub/Sub → Natural Language API → BigQuery

Question 13

A company has a pipeline that uses Vertex AI Pipelines to fetch data from BigQuery, preprocess with Dataflow (without code?), then train an AutoML model, and deploy. However, they want to reduce cloud costs. The pipeline runs hourly. Which change will most reduce compute costs while maintaining throughput?

Accepted Answer

Increase Dataflow batch size to process more data per worker. Option C is correct because increasing the Dataflow batch size allows each worker to process more data per batch, reducing the number of workers needed and the total compute time for the same throughput. This directly lowers Dataflow's compute cost without affecting the pipeline's hourly schedule or the AutoML training budget.

Answer

Decrease the AutoML training budget from 10 to 1 node hour

Answer

Replace Dataflow preprocessing with a Cloud Function that runs on each file upload

Answer

Switch from Vertex AI Pipelines to Cloud Composer for orchestration

Question 14

A small business wants to build a sentiment analysis model for customer reviews without writing any code. They have a small labeled dataset with 500 positive and 500 negative reviews. Which Google Cloud service should they use?

Accepted Answer

AutoML Natural Language. AutoML Natural Language is the correct choice because it allows the business to train a custom sentiment analysis model using their own labeled dataset without writing any code. It provides a low-code interface for uploading data, training, and deploying the model, which aligns with the requirement of no coding and a small labeled dataset.

Answer

Natural Language API

Answer

Vertex AI custom training with PyTorch

Answer

BigQuery ML with logistic regression

Question 15

A healthcare startup deployed a Vertex AI AutoML Vision model to detect anomalies in medical images. The model performs well on the test set but has high latency in production, exceeding the 2-second SLA. The images are stored in Cloud Storage and are processed via a Cloud Function triggered by new uploads. What is the most likely cause?

Accepted Answer

The model is deployed on a small machine type with insufficient compute.. Option B is correct because the most likely cause of high latency exceeding the 2-second SLA is that the Vertex AI AutoML Vision model is deployed on a small machine type (e.g., n1-standard-2 or lower) with insufficient compute resources (CPU/memory). AutoML Vision endpoints use container-based serving, and underpowered machines cannot handle the inference load efficiently, especially for high-resolution medical images, leading to response times beyond the SLA.

Answer

The images are being resized and preprocessed in the Cloud Function, adding latency.

Answer

The Cloud Function has a cold start issue.

Answer

The AutoML Vision endpoint is not using GPU acceleration.