- A
BigQuery ML
Why wrong: BigQuery ML allows creating models using SQL, which may require more coding knowledge than desired. Additionally, deploying models for real-time predictions typically requires extra steps.
- B
Vertex AI Endpoints
Vertex AI Endpoints provides a serverless option to deploy trained models as REST APIs with autoscaling, ideal for real-time predictions without code.
- C
Cloud Functions
Why wrong: Cloud Functions can serve predictions but requires writing code to load the model and handle requests, which is not a low-code solution.
- D
Vertex AI Workbench
Why wrong: Vertex AI Workbench is a notebook-based environment that requires coding to build and train models, which contradicts the low-code requirement.
- E
AutoML Tables
AutoML Tables is a low-code solution for building classification models directly from BigQuery data, with automatic feature engineering and hyperparameter tuning.
Quick Answer
The answer is AutoML Tables and Vertex AI Endpoints. AutoML Tables is the correct low-code binary classification solution because it automatically handles feature engineering for both categorical and numerical columns from BigQuery, trains a model on up to 500,000 rows without requiring any manual code, and directly supports binary classification tasks. Vertex AI Endpoints then allows the trained model to be deployed as a real-time API endpoint with minimal configuration, enabling the analyst to serve predictions without writing custom serving infrastructure. On the Google Professional Machine Learning Engineer exam, this pairing tests your understanding of Google Cloud’s no-code-to-low-code ML pipeline, specifically how to bridge BigQuery data to a production endpoint without scripting. A common trap is choosing Cloud Functions or AI Platform Prediction, but those require more code for model serving. Memory tip: think “AutoML builds, Endpoint serves” — the two services that turn BigQuery data into a live API with zero code.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml 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 data analyst wants to build a binary classification model using a low-code ML solution on Google Cloud. The dataset is stored in BigQuery and contains 500,000 rows with 20 features, including categorical and numerical columns. The analyst has minimal coding experience and needs to deploy the model as an API endpoint for real-time predictions. Which two Google Cloud services should the analyst use to accomplish this task with minimal code? Choose two options.
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 Endpoints
Vertex AI Endpoints is correct because it provides a managed service to deploy trained models as REST API endpoints for real-time predictions with minimal code. The analyst can deploy an AutoML Tables model directly to a Vertex AI Endpoint, enabling low-code deployment and serving.
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
Why it's wrong here
BigQuery ML allows creating models using SQL, which may require more coding knowledge than desired. Additionally, deploying models for real-time predictions typically requires extra steps.
- ✓
Vertex AI Endpoints
Why this is correct
Vertex AI Endpoints provides a serverless option to deploy trained models as REST APIs with autoscaling, ideal for real-time predictions without code.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Functions
Why it's wrong here
Cloud Functions can serve predictions but requires writing code to load the model and handle requests, which is not a low-code solution.
- ✗
Vertex AI Workbench
Why it's wrong here
Vertex AI Workbench is a notebook-based environment that requires coding to build and train models, which contradicts the low-code requirement.
- ✓
AutoML Tables
Why this is correct
AutoML Tables is a low-code solution for building classification models directly from BigQuery data, with automatic feature engineering and hyperparameter tuning.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between model training services (BigQuery ML, AutoML Tables) and model deployment services (Vertex AI Endpoints), leading candidates to incorrectly select BigQuery ML for real-time API deployment when it only supports batch inference.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints automatically handles model versioning, traffic splitting, scaling, and monitoring, and supports gRPC and REST APIs with autoscaling based on request load. AutoML Tables, under the hood, uses neural architecture search and gradient-boosted tree ensembles to train models on tabular data, and the exported model can be directly deployed to an endpoint without writing any serving code.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this PMLE question test?
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Vertex AI Endpoints — Vertex AI Endpoints is correct because it provides a managed service to deploy trained models as REST API endpoints for real-time predictions with minimal code. The analyst can deploy an AutoML Tables model directly to a Vertex AI Endpoint, enabling low-code deployment and serving.
What should I do if I get this PMLE 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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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 30, 2026
This PMLE 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 PMLE exam.
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