GCP ML Engineer Practice Questions - Google Cloud Professional Machine Learning Engineer Exam - Variant 2 - GCP ML Engineer Practice Questions - Google Cloud Professional Machine Learning Engineer Exam

Question 1

A retail company wants to build a product recommendation engine using their existing BigQuery sales data. They have limited ML expertise on their team. Which GCP approach best fits this scenario?

Train a custom TensorFlow model on Vertex AI Training Use BigQuery ML to create a matrix factorization model Deploy a pre-trained model from Model Garden Build a custom pipeline with Kubeflow on GKE

Correct Answer:

Use BigQuery ML to create a matrix factorization model

Question 2

Your team needs to quickly build a document classification system for internal support tickets. The dataset contains 10,000 labeled examples across 15 categories. Which Vertex AI feature provides the fastest path to a production-ready model?

Vertex AI AutoML for text classification Vertex AI custom training with a BERT model Vertex AI Feature Store for ticket embeddings Vertex AI Matching Engine for nearest neighbor lookup

Correct Answer:

Vertex AI AutoML for text classification

Question 3

A data science team stores features in multiple BigQuery tables and Cloud Storage buckets. Different models reuse the same features but compute them independently, leading to training-serving skew. What should you implement to ensure feature consistency?

Create materialized views in BigQuery for each feature Use Vertex AI Feature Store as a centralized feature repository Write Cloud Functions to synchronize feature values hourly Store all features in a single denormalized BigQuery table

Correct Answer:

Use Vertex AI Feature Store as a centralized feature repository

Question 4

You are designing an ML pipeline where multiple teams contribute datasets stored in different GCP projects. You need to track data lineage across these projects and ensure reproducibility. Which service should you use?

Cloud Data Catalog for metadata tagging Vertex ML Metadata for tracking artifacts and lineage Cloud Logging for audit trails BigQuery INFORMATION_SCHEMA for dataset tracking

Correct Answer:

Vertex ML Metadata for tracking artifacts and lineage

Question 5

Your team is developing a fraud detection model. The dataset has 99.5% legitimate transactions and 0.5% fraudulent ones. Which technique should you prioritize to handle this class imbalance?

Increase the training data volume by collecting more legitimate transaction examples Use oversampling of the minority class combined with appropriate evaluation metrics like AUPRC Reduce the model complexity to prevent overfitting on the majority class Remove features that are correlated with the majority class

Correct Answer:

Use oversampling of the minority class combined with appropriate evaluation metrics like AUPRC

Exam overview

About this Exam

Prepare with the GCP ML Engineer Practice Questions - Google Cloud Professional Machine Learning Engineer Exam - Variant 2 practice quiz. This question bank includes 100 questions covering model, vertex, data, need, and feature. Use it to review important concepts, identify knowledge gaps, and build confidence for the related exam, course, or assessment.

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GCP ML Engineer Practice Questions - Google Cloud Professional Machine Learning Engineer Exam - Variant 2

This practice set contains 100 questions from the matching question bank and focuses on model, vertex, data, need, and feature. Work through each question carefully, review the provided solutions, and revisit topics that need more study before your next attempt.

This is an independent study resource intended for practice and review; it is not an official examination or an endorsement by any organization named in the title.

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GCP ML Engineer Practice Questions - Google Cloud Professional Machine Learning Engineer Exam - Variant 2

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