Quick Answer
The answer is the Cloud Natural Language API. This is the correct choice because it offers pre-built text analysis APIs on Google Cloud that include ready-to-use sentiment analysis models, requiring no labeled data or custom model training—the startup simply sends text via an API call and receives a sentiment score and magnitude. On the Google Professional Machine Learning Engineer exam, this question tests your ability to distinguish between fully managed, pre-trained services and custom ML workflows; a common trap is confusing Cloud Natural Language API with AutoML Natural Language, which does require labeled data for training. The key distinction is that pre-built APIs are zero-shot, while AutoML is for custom models. For a memory tip, think “API for instant sentiment, AutoML for custom intent.”
This PMLE practice question tests your understanding of solving business challenges with ml. 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 startup wants to add sentiment analysis to their customer feedback app without any labeled data or custom model training. Which Google Cloud service should they use?
Cloud Natural Language API
Pre-trained model available via API call.
AutoML Natural Language with manual labeling
Why wrong: Requires time and cost for labeling.
Use BigQuery ML to train a text classification model
Why wrong: BigQuery ML is not designed for text sentiment out-of-the-box.
Train a custom sentiment model on Vertex AI
Why wrong: Not necessary; pre-trained API exists.
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
The Cloud Natural Language API provides pre-trained models for sentiment analysis that require no labeled data or custom training. It offers a ready-to-use sentiment analysis feature via a simple API call, making it ideal for a startup that wants to add sentiment analysis without any machine learning expertise or data preparation.
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.
Cloud Natural Language API
Why this is correct
Pre-trained model available via API call.
Related concept
Read the scenario before looking for a memorised answer.
AutoML Natural Language with manual labeling
Why it's wrong here
Requires time and cost for labeling.
Use BigQuery ML to train a text classification model
Why it's wrong here
BigQuery ML is not designed for text sentiment out-of-the-box.
Train a custom sentiment model on Vertex AI
Why it's wrong here
Not necessary; pre-trained API exists.
Common exam traps
Google Cloud often tests the distinction between pre-trained APIs and custom training services, where candidates mistakenly choose AutoML or Vertex AI because they think any ML task requires custom training, overlooking the existence of fully managed, pre-trained APIs like Cloud Natural Language API.
Detailed technical explanation
The Cloud Natural Language API uses a pre-trained deep learning model based on a neural network architecture that has been trained on a large corpus of text to understand sentiment polarity (positive, negative, neutral) and magnitude. It supports multiple languages and can analyze text at the document or entity level, returning a score from -1.0 to 1.0 and a magnitude value. In a real-world scenario, a startup could integrate this API into their customer feedback app with just a few lines of code, processing thousands of feedback entries per second without any ML infrastructure management.
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
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
The correct answer is: Cloud Natural Language API — The Cloud Natural Language API provides pre-trained models for sentiment analysis that require no labeled data or custom training. It offers a ready-to-use sentiment analysis feature via a simple API call, making it ideal for a startup that wants to add sentiment analysis without any machine learning expertise or data preparation.
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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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to use pre-built Google Cloud APIs for text analysis. Which TWO APIs can they use? (Choose TWO.)
easyWhy A: The Cloud Natural Language API provides pre-built machine learning models for text analysis tasks such as entity recognition, sentiment analysis, and syntax analysis. The Cloud Translation API can translate text between languages, which is a form of text analysis. Both are pre-built Google Cloud APIs that directly address the company's need for text analysis without requiring custom model training.
Last reviewed: Jun 30, 2026
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