- A
BigQuery ML, for training and deploying ML models using SQL within BigQuery
Why wrong: BigQuery ML is excellent for training models directly in BigQuery using SQL, but it is limited to BigQuery data and specific algorithm types. It doesn't provide the full experiment tracking, custom training, model monitoring, and multi-version deployment the question describes.
- B
Vertex AI, Google Cloud's unified ML platform covering training, experiment tracking, model registry, deployment, and monitoring in a single managed service
Vertex AI is the complete answer. It provides: managed training (custom containers or AutoML), Vertex AI Experiments (experiment tracking and comparison), Vertex AI Model Registry (version management), Vertex AI Endpoints (serving with traffic splitting for A/B testing), and Model Monitoring (data drift and skew detection). This is Google Cloud's end-to-end ML platform.
- C
Cloud Dataproc, for running distributed Spark ML jobs on managed Hadoop clusters
Why wrong: Cloud Dataproc runs Spark and Hadoop workloads. While Spark has ML libraries (MLlib), Dataproc doesn't provide the integrated experiment tracking, model registry, monitoring, or unified deployment capabilities described.
- D
Cloud Functions, for deploying ML inference code as serverless functions
Why wrong: Cloud Functions can serve ML predictions via HTTP, but it provides only the deployment/serving step. It has no training, experiment tracking, model registry, or monitoring capabilities.
Vertex AI: End-to-End Managed ML Platform with Experiment Tracking
This GCDL practice question tests your understanding of google cloud products, services, and solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning team wants to train, evaluate, deploy, and monitor ML models in a unified platform without managing infrastructure, and with built-in support for experiment tracking, model versioning, and A/B testing between model versions. Which Google Cloud product provides this end-to-end managed ML platform?
Quick Answer
Vertex AI is the correct choice because it is Google Cloud’s end-to-end managed ML platform that unifies training, experiment tracking, model versioning, deployment, and monitoring into a single service, eliminating the need to manage underlying infrastructure. This directly meets the requirement for built-in experiment tracking, a model registry for versioning, and support for A/B testing between model versions—all without manual setup. On the Google Cloud Digital Leader exam, this question tests your understanding of how Vertex AI simplifies the ML lifecycle for teams that want a fully managed, integrated solution rather than assembling separate tools. A common trap is confusing Vertex AI with standalone services like AI Platform or AutoML, but remember that Vertex AI is the umbrella platform that includes those capabilities. Memory tip: think of Vertex AI as the “one-stop shop” for ML—if the scenario mentions experiment tracking, versioning, and A/B testing together, the answer is always Vertex AI.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Vertex AI, Google Cloud's unified ML platform covering training, experiment tracking, model registry, deployment, and monitoring in a single managed service
Vertex AI is Google Cloud's unified ML platform that provides an end-to-end managed service for training, evaluating, deploying, and monitoring ML models without requiring infrastructure management. It includes built-in experiment tracking, a model registry for versioning, and supports A/B testing between model versions, directly matching the question's requirements.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
BigQuery ML, for training and deploying ML models using SQL within BigQuery
Why it's wrong here
BigQuery ML is excellent for training models directly in BigQuery using SQL, but it is limited to BigQuery data and specific algorithm types. It doesn't provide the full experiment tracking, custom training, model monitoring, and multi-version deployment the question describes.
- ✓
Vertex AI, Google Cloud's unified ML platform covering training, experiment tracking, model registry, deployment, and monitoring in a single managed service
Why this is correct
Vertex AI is the complete answer. It provides: managed training (custom containers or AutoML), Vertex AI Experiments (experiment tracking and comparison), Vertex AI Model Registry (version management), Vertex AI Endpoints (serving with traffic splitting for A/B testing), and Model Monitoring (data drift and skew detection). This is Google Cloud's end-to-end ML platform.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Dataproc, for running distributed Spark ML jobs on managed Hadoop clusters
Why it's wrong here
Cloud Dataproc runs Spark and Hadoop workloads. While Spark has ML libraries (MLlib), Dataproc doesn't provide the integrated experiment tracking, model registry, monitoring, or unified deployment capabilities described.
- ✗
Cloud Functions, for deploying ML inference code as serverless functions
Why it's wrong here
Cloud Functions can serve ML predictions via HTTP, but it provides only the deployment/serving step. It has no training, experiment tracking, model registry, or monitoring capabilities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse BigQuery ML's SQL-based model training with a full ML platform, overlooking its lack of experiment tracking, model versioning, and A/B testing capabilities that Vertex AI provides.
Detailed technical explanation
How to think about this question
Vertex AI integrates multiple components like Vertex AI Experiments for tracking hyperparameters and metrics, Vertex AI Model Registry for versioning and lineage, and Vertex AI Endpoints for deploying models with traffic splitting for A/B testing. Under the hood, it uses a managed infrastructure layer with autoscaling and GPU/TPU support, and the A/B testing feature leverages endpoint traffic routing percentages to direct a fraction of requests to different model versions for evaluation.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this GCDL question test?
Google Cloud products, services, and solutions — This question tests Google Cloud products, services, and solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI, Google Cloud's unified ML platform covering training, experiment tracking, model registry, deployment, and monitoring in a single managed service — Vertex AI is Google Cloud's unified ML platform that provides an end-to-end managed service for training, evaluating, deploying, and monitoring ML models without requiring infrastructure management. It includes built-in experiment tracking, a model registry for versioning, and supports A/B testing between model versions, directly matching the question's requirements.
What should I do if I get this GCDL question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.
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