Quick Answer
The answer is Cloud Natural Language API and AutoML Natural Language. These two Google Cloud services are appropriate for low-code text analysis because they allow a data analyst to extract insights from unstructured text without writing custom machine learning code. The Cloud Natural Language API provides pre-trained models for tasks like sentiment analysis, entity recognition, and syntax analysis through simple API calls, while AutoML Natural Language enables custom model training with minimal code by using a graphical interface to label and train on domain-specific text. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish between fully managed, pre-built services and those requiring custom training—a common trap is confusing Cloud Natural Language API with Vertex AI’s custom training pipelines, which demand more coding expertise. Remember the memory tip: “API for instant, AutoML for custom”—if the analyst needs immediate results without training, choose the API; if they need tailored analysis with a low-code workflow, choose AutoML Natural Language.
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 use low-code ML to analyze text data. Which TWO Google Cloud services are appropriate?
Vertex AI Workbench
Why wrong: Wrong: Requires coding, not low-code.
Document AI
Why wrong: Wrong: For document processing, not general text analysis.
Cloud Natural Language API
Correct: Pre-trained sentiment and entity analysis via API.
AutoML Natural Language
Correct: No-code text classification and entity extraction.
BigQuery ML for sentiment
Why wrong: Wrong: BigQuery ML does not directly support text sentiment models.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Cloud Natural Language API
Cloud Natural Language API is a low-code ML service that provides pre-trained models for analyzing text, including sentiment analysis, entity recognition, and syntax analysis, without requiring custom model training. It is appropriate for a data analyst who wants to quickly extract insights from text data using simple API calls.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Vertex AI Workbench
Why it's wrong here
Wrong: Requires coding, not low-code.
Document AI
Why it's wrong here
Wrong: For document processing, not general text analysis.
Cloud Natural Language API
Why this is correct
Correct: Pre-trained sentiment and entity analysis via API.
Related concept
Read the scenario before looking for a memorised answer.
AutoML Natural Language
Why this is correct
Correct: No-code text classification and entity extraction.
Related concept
Read the scenario before looking for a memorised answer.
BigQuery ML for sentiment
Why it's wrong here
Wrong: BigQuery ML does not directly support text sentiment models.
Common exam traps
The trap here is that candidates may confuse BigQuery ML's sentiment analysis feature (which is SQL-based and not a dedicated low-code service) with a standalone low-code ML service, or mistakenly think Vertex AI Workbench is low-code when it actually requires coding in Python or other languages.
Detailed technical explanation
Cloud Natural Language API uses pre-trained models based on deep learning architectures like BERT, enabling it to perform entity sentiment analysis and content classification with high accuracy. AutoML Natural Language extends this by allowing users to train custom models on their own labeled datasets using transfer learning, which is still low-code as it only requires uploading data and setting training parameters via the console or API. A real-world scenario is a marketing analyst analyzing customer reviews: they can use Cloud Natural Language API for out-of-the-box sentiment analysis, or AutoML Natural Language to train a custom model for domain-specific terms like 'battery life' in electronics reviews.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Natural Language API — Cloud Natural Language API is a low-code ML service that provides pre-trained models for analyzing text, including sentiment analysis, entity recognition, and syntax analysis, without requiring custom model training. It is appropriate for a data analyst who wants to quickly extract insights from text data using simple API calls.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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