PMLE Architecting Low-Code ML Solutions Practice Question
An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?
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
✓
Use the Natural Language API
The Natural Language API provides pre-built sentiment analysis with minimal setup. AutoML NLP would require custom training, BigQuery ML is for tabular data, and Vertex AI Prediction needs a deployed model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Vertex AI Prediction with a pre-trained model
Why it's wrong here
Vertex AI Prediction only serves a model; it performs no sentiment analysis itself, so a pre-trained NLP model must still be selected and deployed. It tempts when a trained model already exists and only hosting is required.
- ✗
Use BigQuery ML with LOGISTIC_REG
Why it's wrong here
BigQuery ML's LOGISTIC_REG trains a bespoke classifier, so you must supply labelled data, engineer features and tune the model — the customisation the scenario excludes. It suits predicting a known binary outcome from structured columns. Sentiment analysis of raw review text needs a pre-trained natural language model, which returns scores without training.
- ✗
Train a custom model using AutoML NLP
Why it's wrong here
Training a custom AutoML NLP model demands labelled data, compute budget and training time, none of which the scenario's minimal-customisation requirement permits. AutoML suits domain-specific classification where prebuilt models underperform and labelled corpora already exist. Here, prebuilt sentiment analysis returns results immediately without training.
- ✓
Use the Natural Language API
Why this is correct
The Natural Language API provides pre-trained sentiment analysis, satisfying the minimal-customisation constraint without labelled data or model training. It handles large text volumes through a managed endpoint, unlike custom model approaches that demand dataset preparation and tuning. This makes it the most efficient fit for the stated scenario.
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Same concept, more angles
1 more way this is tested on PMLE
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 needs to analyze customer feedback from app reviews to identify common themes and sentiment. They have millions of reviews in multiple languages. Which combination of pre-built APIs should they use?
medium- A.Speech-to-Text then Natural Language API
- B.Natural Language API only
- ✓ C.Translation API then Natural Language API
- D.Vision API then Natural Language API
Why C: The reviews are text in multiple languages, so the Translation API is needed to convert them into a single language (e.g., English) that the Natural Language API can analyze for sentiment and entity/theme extraction. The Natural Language API supports multiple languages for sentiment, but its entity and syntax analysis is optimized for English, and translation ensures consistent theme detection across all reviews. Combining Translation API then Natural Language API directly addresses the multilingual text requirement.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.