PMLE Practice Test 2 — 30 Questions

Question 1

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?

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

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

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BigQuery ML with logistic regression

Question 2

A team is using AI Platform Data Labeling Service to label data for a classification model. They want to allow a labeler from a different team to work on the same dataset. What is the correct way to grant access?

Accepted Answer

Add the labeler as a participant in the labeling task and assign IAM roles on the dataset. Labeling tasks are shared by granting the labeler role on the dataset resource. Option A is wrong because sharing the entire project gives too much access. Option B is wrong because the Data Labeling Service does not use Cloud Storage ACLs for task access. Option C is wrong because exporting and reimporting causes duplication.

Answer

Add the labeler's account as a Project Editor on the project

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Share the Cloud Storage bucket containing the data with the labeler

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Export the dataset and have the labeler create a new dataset

Question 3

A team has developed a prototype of a recommendation model using a small dataset on a single VM. They need to scale to a larger dataset for production training. They plan to use Vertex AI training with a custom container. What is the best practice for handling the increased data volume?

Accepted Answer

Use TFRecord format and streaming reads.. Using TFRecord format with streaming reads allows efficient, scalable data loading from Cloud Storage, reducing memory pressure and improving I/O performance. Option A is wrong because increasing batch size to maximum can cause memory issues and may not improve throughput. Option C is wrong because storing all data in memory is not scalable. Option D is wrong because a single powerful VM still has limits and is not cost-effective for large datasets.

Answer

Increase the batch size to maximum.

Answer

Store all data in memory before training.

Answer

Use a single powerful VM with high memory.

Question 4

A company needs to extract entities (e.g., names, dates) from customer emails using a pre-trained model. Which service should they use?

Accepted Answer

Natural Language API. The Natural Language API provides entity extraction as a pre-trained model. Vision API is for images, Translation API for translation, and Dialogflow for conversational agents.

Answer

Translation API

Answer

Dialogflow

Answer

Vision API

Question 5

A team uses Cloud Build to automatically trigger a Vertex AI pipeline when changes are pushed to the model code repository. They have a cloudbuild.yaml file that builds a container image and submits the pipeline. However, they want to run the pipeline only if the commit includes changes to the 'training/' directory. Which Cloud Build configuration option should be used to filter the trigger?

Accepted Answer

Set the 'includedFiles' field to 'training/**' in the trigger configuration.. Cloud Build triggers support an `includedFiles` field that specifies a glob pattern. When set to `training/**`, the trigger will only fire if the commit includes changes to files under the `training/` directory. This is the native, declarative way to filter triggers based on changed file paths without additional infrastructure.

Answer

Add a 'ignoreFiles' field with 'training/**' to the trigger.

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Use a 'substitutions' field with a regex pattern to filter commits.

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Configure a Cloud Function to check the commit diff and call Cloud Build API conditionally.

Question 6

Which TWO actions can help reduce the latency of online prediction requests for a deep learning model served on Vertex AI?

Accepted Answer

Use a GPU accelerator for the deployed model.. Using a GPU accelerator speeds up inference, and batching requests reduces overhead per request. Minimizing replicas doesn't help latency; increasing CPU doesn't always help if GPU is better.

Answer

Increase the number of CPU vCPUs per machine.

Answer

Set min_replica_count to 0 to avoid idle instances.

Answer

Decrease the number of replicas to reduce resource contention.

Question 7

A retail company wants to build a recommendation system to show 'frequently bought together' items. Which Recommendations AI model type should they use?

Accepted Answer

frequently-bought-together. The 'frequently-bought-together' model type in Google Cloud Recommendations AI is specifically designed to identify items that are commonly purchased in the same transaction, using co-occurrence analysis of historical purchase data. This directly matches the requirement to show items that are frequently bought together, leveraging association rule mining (e.g., Apriori algorithm) to generate recommendations.

Answer

recently-viewed

Answer

recommended-for-you

Answer

others-you-may-like

Question 8

You are deploying a scikit-learn model for online predictions. The model size is 200 MB. You want to minimize latency and cost. Which serving option should you choose?

Accepted Answer

Deploy to Vertex AI online prediction using a prebuilt container for scikit-learn.. Vertex AI online prediction with custom containers is suitable for scikit-learn models. Vertex AI will host the container and scale. Using AI Platform or Cloud Functions with a 200 MB model might hit limits.

Answer

Use Cloud Run with a custom container.

Answer

Create a Kubernetes cluster on GKE and deploy the model there.

Answer

Export the model as a Cloud Function.

Question 9

A team is building a CI/CD pipeline for an ML model. They want to automatically trigger a Vertex AI pipeline for retraining whenever new training data arrives in a Cloud Storage bucket, but only if a specific Pub/Sub notification is published by a data ingestion process. Which approach meets these requirements with minimal operational overhead?

Accepted Answer

Use Eventarc to route the Pub/Sub notification to a Cloud Function that calls the Vertex AI pipeline creation API.. Eventarc can directly listen to a Pub/Sub topic and route matching messages to a Cloud Function, which then calls the Vertex AI pipeline creation API. This serverless approach triggers the pipeline only when the specific Pub/Sub notification is published, meeting the requirement with zero infrastructure to manage and no polling overhead.

Answer

Use Cloud Scheduler to run a job every hour that checks for new files in Cloud Storage and starts the pipeline if new files exist.

Answer

Configure a Cloud Build trigger that listens to the Pub/Sub topic and executes a build step that submits the pipeline run.

Answer

Create a Dataflow streaming pipeline that reads from Pub/Sub and triggers the Vertex AI pipeline via a custom sink.

Question 10

Your team is serving a large language model on Vertex AI using a custom container. The endpoint experiences intermittent 502 errors during traffic spikes. The autoscaling configuration uses a CPU utilization target of 60% and the model is deployed on n1-standard-4 instances. The model requires significant memory. Which combination of changes is most likely to resolve the issue?

Accepted Answer

Switch to a machine type with more memory, e.g., n1-highmem-8, and increase min_replica_count.. The 502 errors likely indicate the instances are overwhelmed or timing out. Increasing the machine type to a high-memory instance reduces memory pressure, and adding more replicas through a lower target scaling metric or higher min replicas provides capacity. Tuning batch size helps but is secondary. GPU may not help if the issue is memory.

Answer

Increase the target CPU utilization to 90% to allow more requests per instance.

Answer

Enable canary traffic splitting to reduce load on the main endpoint.

Answer

Reduce the model batch size from 32 to 1 to lower memory per request.

Question 11

Your team deploys a multi-model endpoint on Vertex AI with two models: Model A (small, low latency) and Model B (large, high latency). You configure traffic splitting so that 90% goes to Model A and 10% to Model B. However, you notice that the latency for Model A increases when Model B receives traffic. What is the most likely cause?

Accepted Answer

The models are collocated on the same instances, leading to resource contention.. In a multi-model endpoint, all models share the underlying infrastructure. When Model B handles requests, it consumes resources (CPU/memory), causing contention that degrades Model A's latency. Collocation of models on the same instance is the issue.

Answer

Model A is being overloaded because autoscaling is based on aggregate traffic.

Answer

The traffic split is misconfigured, causing requests to be routed incorrectly.

Answer

Model B's logging is generating too much output, slowing down the predictor.

Question 12

A data scientist deployed a model to Vertex AI Prediction. When making a prediction request as shown in the exhibit, they receive a 400 error. What is the most likely cause?

Accepted Answer

The model was trained on 2 features, but the request provides 3 features.. The 400 error indicates a malformed request, typically due to a mismatch between the input features the model expects and what is provided. Since the model was trained on 2 features but the request includes 3 features, Vertex AI rejects the prediction as invalid input shape mismatch. This is the most common cause of 400 errors in Vertex AI Prediction when the instance structure does not match the model's signature.

Answer

The request JSON is malformed due to a missing comma between instances.

Answer

The endpoint path is incorrect; it should include the model version.

Answer

The request is sending 3 separate instances but the model expects only 1.

Question 13

A data scientist uses Vertex AI Pipelines to orchestrate an ML workflow. They want to reuse a component from Google's curated repository. What is the recommended way to incorporate it?

Accepted Answer

Use prebuilt components from the Google Cloud Pipeline Components repository. Google provides a curated set of prebuilt components in the Google Cloud Pipeline Components repository, which are designed to be directly imported and used within Vertex AI Pipelines. These components encapsulate common ML tasks (e.g., model training, deployment) and are maintained by Google, ensuring compatibility and reducing custom code. Using them is the recommended approach to avoid reinventing the wheel and to leverage Google's best practices.

Answer

Import the component from Google Cloud Build

Answer

Use the 'aiplatform' Python SDK to define the component

Answer

Copy the component code into the pipeline definition

Question 14

A media company wants to transcribe audio files from customer support calls into text for analysis. The audio is in English with clear speech and no background noise. They want a quick solution with no ML model training. Which Google Cloud service should they use?

Accepted Answer

Speech-to-Text API with the latest_long model. Speech-to-Text is a pre-built API for transcribing audio to text. It is ready to use without training. AutoML NLP is for text classification, not transcription. Vertex AI Workbench and Translation API are not relevant.

Answer

Translation API to translate the audio

Answer

AutoML NLP to train a transcription model

Answer

Vertex AI Workbench to train a custom speech recognition model

Question 15

Which TWO actions are recommended to detect and mitigate data drift in a production ML system on Vertex AI?

Accepted Answer

Automatically retrain the model when drift exceeds thresholds. Vertex AI's automated retraining pipeline can be triggered when data drift exceeds a predefined threshold, ensuring the model adapts to distribution changes without manual intervention. Option D is correct because Vertex AI Model Monitoring continuously tracks feature distribution statistics (e.g., using Jensen-Shannon divergence or L-infinity distance) and sends alerts when drift is detected, enabling proactive mitigation.

Answer

Deploy multiple models and use an ensemble to average predictions

Answer

Manually review model predictions daily

Answer

Monitor prediction errors and flag when confidence is low

Question 16

You are responsible for maintaining an ML pipeline that runs daily on Vertex AI Pipelines. The pipeline preprocesses data, trains a model, and deploys it to an endpoint. Recently, the pipeline has been failing at the deployment step because the endpoint already exists and the deploy step tries to create a new endpoint instead of updating the existing one. The pipeline code is written using the Kubeflow Pipelines SDK. You need to modify the pipeline to resolve this issue with minimal changes. What should you do?

Accepted Answer

In the deployment component, add a check to verify if the endpoint exists, and if so, call the update endpoint method instead of create.. The issue is that the deployment component always tries to create a new endpoint, which fails if the endpoint already exists. To resolve this with minimal changes, the deployment component should first check if the endpoint exists. If it does, it should update the existing endpoint instead of creating a new one. This approach aligns with best practices for CI/CD pipelines. Option A is incorrect because introducing a Cloud Function bypasses the pipeline orchestration and adds unnecessary complexity. Option C is incorrect because retrying will not fix the fundamental issue of attempting to create an existing endpoint. Option D is incorrect because manually deleting the endpoint before each run defeats automation and is not a robust solution.

Answer

Change the pipeline to use a Cloud Function that triggers the deployment independently, bypassing Vertex AI Pipelines.

Answer

Set the deploy component's retry policy to infinite so it eventually succeeds.

Answer

Manually delete the existing endpoint before each pipeline run.

Question 17

You are a machine learning engineer at a financial technology company. You have deployed a complex ensemble model consisting of three sub-models (XGBoost, TensorFlow, and PyTorch) for real-time fraud detection. The model is served on Vertex AI online prediction with a custom container that orchestrates the three models sequentially. The endpoint currently uses n1-highmem-8 machines with no accelerators. You are experiencing high latency (avg 500ms) during peak trading hours (9:30 AM - 4:00 PM EST), exceeding the 200ms SLA. The container is CPU-bound, and memory usage is around 60%. The model weights total 500 MB. You have already tried increasing the batch size per request from 1 to 4, which reduced latency slightly but not enough. The traffic pattern is very spiky, with sudden bursts of up to 1000 requests per second. Your goal is to meet the latency SLA without significantly increasing cost. Which action should you take?

Accepted Answer

Add a NVIDIA T4 GPU accelerator to the existing machine type.. Adding a GPU accelerator (e.g., NVIDIA T4) to the instances can significantly speed up the TensorFlow and PyTorch components, which are deep learning models. The XGBoost part runs on CPU but the overall latency bottleneck is likely the deep learning models. GPU will accelerate inference of those models, reducing total latency. Increasing CPUs will help only marginally as the main bottleneck is compute. Reducing min replicas may increase cold start and queue. Switching to batch prediction changes the model from real-time to batch, which does not meet the latency requirement.

Answer

Reduce the min_replica_count to 0 to allow scaling down aggressively and add more replicas during spikes.

Answer

Increase the machine type to n1-highmem-16 with more vCPUs.

Answer

Switch the model to Vertex AI batch prediction and run predictions every hour.

Question 18

A team prototypes a recommendation model using a Jupyter notebook on Vertex AI Workbench. They want to productionize the model with CI/CD. Which approach should they use to package the model for deployment?

Accepted Answer

Upload the model to Vertex AI Model Registry and use it for deployment. Vertex AI Model Registry is the central repository for managing ML models, enabling versioning, evaluation, and deployment to endpoints. This approach integrates with CI/CD pipelines via the Vertex AI SDK or Cloud Build, allowing automated model promotion and deployment without manual packaging. Option D directly leverages Vertex AI's native deployment workflow, which is the recommended path for productionizing models from Workbench.

Answer

Use Cloud Build to deploy the notebook directly as a prediction endpoint

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Store the model in Cloud Source Repositories and deploy from there

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Containerize the model and push to Artifact Registry, then deploy via Cloud Run

Question 19

A company implements an ML pipeline using Vertex AI Pipelines. The pipeline trains a model using custom training jobs and then deploys it to an endpoint. The team notices that the endpoint occasionally serves an older model version for a few minutes after a new pipeline run completes. What is the most likely cause?

Accepted Answer

The endpoint is configured with a canary traffic split, and the old model is still receiving a fraction of traffic during the rollout.. D is correct because Vertex AI endpoints can be configured with a canary (gradual) traffic rollout strategy. When a new model is deployed, traffic is shifted incrementally from the old model to the new one over a specified duration. During this rollout window, the old model continues to serve a fraction of requests, which explains why users occasionally see the older model version for a few minutes after the pipeline completes.

Answer

The new model artifact is temporarily unavailable, so the endpoint falls back to the previous version.

Answer

The prediction cache is returning cached results from the old model.

Answer

The pipeline failed to update the endpoint with the new model ID.

Question 20

A company runs a Vertex AI pipeline that uses a container component to preprocess data. The component downloads a large file from a public URL and saves the output to Cloud Storage. The pipeline fails intermittently with a 'timeout' error. Which THREE steps should the team take to improve reliability? (Choose three.)

Accepted Answer

Make the component idempotent by checking for existing output before processing.. Making the component idempotent by checking for existing output before processing prevents redundant work and avoids timeout failures when the file has already been downloaded. In Vertex AI pipelines, idempotent components can safely skip processing if the output already exists in Cloud Storage, reducing the risk of hitting timeout limits on subsequent pipeline runs.

Answer

Reduce the size of the file being downloaded.

Answer

Increase the machine type for the component.