PMLE Practice Test 38 — 15 Questions

Question 1

Your organization wants to automate the retraining of a model when new data is available and also on a weekly schedule. Which TWO services would you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Functions. Cloud Scheduler (E) is used to trigger the retraining on a weekly schedule by sending a message to a Pub/Sub topic or making an HTTP request. Cloud Functions (A) is the serverless compute service that executes the retraining code in response to that trigger, and it can also be triggered directly when new data arrives (e.g., via Cloud Storage or Pub/Sub). Together, they provide both event-driven and scheduled automation without managing infrastructure.

Answer

Cloud Composer

Answer

Cloud Tasks

Answer

Dataflow

Question 2

A data science team uses Cloud Composer to orchestrate ML workflows. They need to trigger a Vertex AI pipeline after a BigQuery data load completes, and then run a Dataflow job. Which Airflow operator should they use to launch the Vertex AI pipeline?

Accepted Answer

VertexAIPipelineJobOperator. The VertexAIPipelineJobOperator is specifically designed to trigger a Vertex AI pipeline run from within an Airflow DAG. This operator directly corresponds to the requirement of launching a Vertex AI pipeline after a BigQuery data load completes, making it the appropriate choice for orchestrating ML workflows with Cloud Composer.

Answer

DataflowStartFlexTemplateJobOperator

Answer

VertexAICreateCustomJobOperator

Answer

MLEngineTrainingOperator

Question 3

The pipeline fails during the evaluate component with error "Model not found". What is the most likely cause?

Accepted Answer

The model_id parameter is referencing the wrong output. The error 'Model not found' during the evaluate component indicates that the model_id parameter is referencing an output that does not exist or is incorrectly named. In Vertex AI Pipelines, the evaluate component takes the model artifact from a previous training step via an output parameter or artifact reference. If the model_id parameter points to a wrong output (e.g., a different step's output or a misspelled reference), the pipeline cannot locate the model. This is the most likely cause because the error is specific to model resolution, not dataset or threshold issues.

Answer

The dataset_id is misspelled

Answer

The training container did not produce a model artifact

Answer

The threshold value is invalid

Question 4

An ML engineer is optimizing a large model for deployment on Vertex AI with GPU acceleration. They want to reduce model size and improve inference latency without significant accuracy loss. Which tool should they use?

Accepted Answer

Use Vertex AI Model Optimization with TensorRT.. Vertex AI Model Optimization with TensorRT is specifically designed to reduce model size and improve inference latency on NVIDIA GPUs by applying techniques like quantization, pruning, and graph optimizations. TensorRT optimizes the model for the target GPU architecture, enabling faster inference with minimal accuracy loss, which directly addresses the engineer's goals.

Answer

Use gcloud CLI to prune the model.

Answer

Use Cloud TPU for faster inference.

Answer

Use TensorFlow.js converter to optimize the model for web.

Question 5

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

Answer

JumpStart only supports text-based models

Answer

JumpStart only supports tabular data models

Question 6

A team runs a Vertex AI pipeline daily. They notice that a component that downloads a file from a public URL always executes even when the URL and parameters haven't changed. They want to avoid unnecessary re-execution and reduce costs. What should they do?

Accepted Answer

Use a pre-built Google Cloud Pipeline Component for file download.. Using a pre-built Google Cloud Pipeline Component for file download ensures the component is deterministic and has caching enabled by default, which prevents unnecessary re-execution when inputs haven't changed. Option A is a valid approach but less direct; ensuring determinism and checking caching settings is generic advice, whereas using a pre-built component immediately resolves both issues. Option B is incorrect because 'enable_caching=True' is not a valid parameter on the pipeline decorator. Option D is unnecessary because custom cache keys are not needed if the default caching works.

Answer

Ensure the component is deterministic and that caching is not disabled for that component.

Answer

Enable pipeline caching by setting 'enable_caching=True' on the pipeline decorator.

Answer

Use the 'dsl.CachingOptions' to set a custom cache key for the component.

Question 7

Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI and uses BigQuery for feature engineering. Recently, the pipeline has been taking significantly longer to execute. Upon investigation, you find that the BigQuery query for feature extraction is being rerun every time the pipeline runs, even though the underlying data hasn't changed. The pipeline is scheduled to run every hour. You want to reduce cost and execution time without losing the ability to detect data drifts. Which approach should you take?

Accepted Answer

Move the feature extraction to a separate scheduled query in BigQuery and load the results into a table that the pipeline reads from.. It decouples the feature extraction from the training pipeline by using a separate scheduled BigQuery query that writes results to a table. This eliminates redundant query execution on every pipeline run, reducing cost and execution time, while the scheduled query can be set to run at a frequency that still detects data drifts (e.g., hourly). The pipeline then reads from the precomputed table, avoiding repeated full scans of the source data.

Answer

Implement a caching mechanism in the pipeline that stores the results of the BigQuery query and reuses them if the data hasn't changed.

Answer

Reduce the pipeline frequency to once a day to minimize the number of runs.

Answer

Use a conditional pipeline that checks if the data has changed before running the feature extraction step.

Question 8

Your team has built a low-latency similarity search service using Vertex AI Matching Engine (Vector Search). The index is updated daily with new embeddings. You need to serve the latest index without downtime. What is the correct deployment strategy?

Accepted Answer

Create a new index version, deploy it to the same endpoint, and then update the endpoint to use the new index version.. Vertex AI Matching Engine supports deploying a new index version to the same endpoint without downtime. You create a new index version from the updated embeddings, deploy it to the existing endpoint, and then update the endpoint to use the new version. This allows traffic to seamlessly switch to the updated index once it is fully deployed, avoiding any service interruption.

Answer

Use streaming updates to continuously update the index.

Answer

Update the existing index in place by calling the index update API.

Answer

Delete the old index and create a new index each day, then deploy the new index to a new endpoint and update DNS.

Question 9

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 10

A data scientist wants to quickly experiment with a pre-trained Vision Transformer model from Hugging Face and fine-tune it on a custom dataset using Vertex AI. They want to use a managed environment with minimal setup. Which Vertex AI service should they use?

Accepted Answer

Vertex AI JumpStart. Vertex AI JumpStart is the correct choice because it provides a managed environment with pre-built, optimized containers for popular models like Vision Transformers, enabling one-click deployment and fine-tuning with minimal setup. It abstracts away infrastructure management, allowing the data scientist to quickly experiment without configuring custom training scripts or environments.

Answer

Vertex AI Prediction

Answer

Vertex AI Workbench

Answer

AI Platform Training

Question 11

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

''

Answer

'experiment'

Answer

'latest'

Question 12

A team deploys a model using Vertex AI Endpoint with automatic scaling. They observe that during traffic spikes, new instances take a long time to become ready, causing high latency for some requests. What should they configure to reduce this startup time?

Accepted Answer

Use a custom container with a smaller footprint. Using a custom container with a smaller footprint reduces the time to pull and initialize the container, thereby decreasing startup latency during traffic spikes. Option A (increasing max replicas) does not affect startup time; it only increases the upper limit. Option C (predictive autoscaling) can help anticipate spikes but does not reduce container startup time. Option D (higher target CPU utilization) delays scaling, which may worsen latency.

Answer

Increase the max replicas

Answer

Enable predictive autoscaling

Answer

Set a higher target CPU utilization

Question 13

A healthcare provider needs to extract structured information from incoming PDF forms (e.g., patient intake forms). They want to automate data extraction without writing custom models. Which Google Cloud service should they use?

Accepted Answer

Document AI with a form parser processor. Document AI with a form parser processor is the correct choice because it is purpose-built for extracting structured data from PDF forms, including key-value pairs and tables, without requiring custom model development. It uses pre-trained models specifically for form understanding, making it ideal for automating intake form processing.

Answer

Natural Language API for entity extraction

Answer

Vision API

Answer

AutoML Vision for object detection

Question 14

A data engineer wants to use BigQuery ML to train a model that predicts customer churn using a table with customer features and a label column. They want to use a deep neural network. Which model type should they specify?

Accepted Answer

DNN_CLASSIFIER. The DNN_CLASSIFIER model type in BigQuery ML is specifically designed for classification tasks using a deep neural network architecture. Since the problem is predicting customer churn (a binary classification problem) and the data engineer explicitly wants to use a deep neural network, DNN_CLASSIFIER is the appropriate choice.

Answer

BOOSTED_TREE_CLASSIFIER

Answer

LOGISTIC_REG

Answer

DNN_REGRESSOR

Question 15

A machine learning engineer is monitoring a deployed churn prediction model that has shown a gradual decline in accuracy over the past month. The engineer wants to diagnose the root cause of the performance degradation. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Use Vertex AI Model Monitoring to detect data drift by comparing the distribution of recent input features against the training data distribution.. Vertex AI Model Monitoring is specifically designed to detect data drift by comparing the distribution of recent input features against the training data distribution. This allows the engineer to identify if the gradual decline in accuracy is caused by changes in the input data, which is a common root cause for model performance degradation over time.

Answer

Increase the model's learning rate and fine-tune it on the latest data.

Answer

Immediately retrain the model using all available historical data to improve accuracy.

Answer

Deploy a second model in parallel to compare predictions.