PMLE Practice Test 2 — 25 Questions

Question 1

You are fine-tuning a large language model (LLM) from Vertex AI Model Garden using a custom dataset. You need to minimize training cost while maintaining reasonable throughput. Which THREE strategies should you combine?

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

Accepted Answer

Use spot VM instances for training. Spot VM instances are significantly cheaper than on-demand instances, reducing training cost. They can be preempted, but for fine-tuning tasks that can checkpoint and resume, this trade-off is acceptable for cost savings.

Answer

Use full fine-tuning of all model parameters

Answer

Use TPU v4 pods for training

Question 2

You need to query a Vertex AI Vector Search index for nearest neighbours. The index is deployed on an endpoint. Which API method should you use to perform the query?

Accepted Answer

projects.locations.indexEndpoints.findNeighbors. The correct API method to query a deployed Vertex AI Vector Search index for nearest neighbors is `projects.locations.indexEndpoints.findNeighbors`. This method is specifically designed for vector similarity search against an index endpoint, returning the nearest neighbors for a given query vector. The other options either target the wrong resource (indexes instead of indexEndpoints) or use methods intended for different purposes like model prediction.

Answer

projects.locations.indexes.match

Answer

projects.locations.indexes.query

Answer

projects.locations.endpoints.predict

Question 3

You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?

Accepted Answer

Write the data to Cloud Storage and pass the GCS URI as an artifact.. In KFP SDK v2, passing large datasets between components is best done by writing the data to Cloud Storage and passing the GCS URI as an artifact. This approach leverages KFP's built-in artifact tracking, ensures data persistence across container restarts, and avoids memory or disk limitations of ephemeral containers. The artifact is automatically serialized and passed as an input/output parameter, enabling efficient, scalable data exchange.

Answer

Use the component's temporary directory to share data between containers.

Answer

Pass the data as a serialized Python object in memory.

Answer

Store the data in a BigQuery table and pass the table reference.

Question 4

A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?

Accepted Answer

ML.EVALUATE. ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.

Answer

ML.PREDICT

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ML.FEATURE_IMPORTANCE

Answer

ML.TRAIN

Question 5

A financial company is building a fraud detection model. The dataset has 1% fraud cases and 99% legitimate transactions. Which technique should they use to handle the class imbalance?

Accepted Answer

Use class weighting or synthetic oversampling (SMOTE) during training. Class weights or resampling techniques like SMOTE are standard for imbalanced datasets. Option A is correct. Option B (undersampling majority) can lose information. Option C (collect more data) is impractical. Option D (no alterations) will bias the model.

Answer

Randomly undersample the majority class to balance the dataset

Answer

Collect more data until the fraud rate increases

Answer

Train without any modifications; the model will naturally handle it

Question 6

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 7

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

Answer

Share the Cloud Storage bucket containing the data with the labeler

Answer

Export the dataset and have the labeler create a new dataset

Question 8

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 9

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 10

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.

Answer

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

Answer

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

Question 11

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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