Translate API & Preprocessing — PMLE Practice Question
A representative Google Cloud Professional Machine Learning Engineer (PMLE) exam question on Translate API & Preprocessing. Work through it below, then read why each option is right or wrong.
This question is part of the free Google Cloud Professional Machine Learning Engineer practice test.
Short answer
The correct answer is D. Use Dataflow with Apache Beam to read from BigQuery; apply Translate API for translation; use natural language processing libraries for sentiment/topic extraction; deduplicate and filter using Beam; write to BigQuery..
Dataflow with Apache Beam is the most scalable and efficient approach for this multi-stage pipeline. It handles translation via Translate API, NLP for sentiment/topic extraction, deduplication, and filtering in a single distributed system, then writes clean data to BigQuery. Option D is the only approach that integrates all preprocessing steps in a scalable pipeline. Option B covers translation and deduplication but has no step for sentiment and topic extraction. Option C (Workbench) is not scalable for 1 TB. Option A (Cloud Run) is inefficient for batch processing and adds unnecessary Firestore writes.
The Question
Your team has collected 1 TB of multilingual customer feedback (text in 12 languages) stored in BigQuery. You need to preprocess this data by: (1) translating all text to English, (2) extracting sentiment and key topics, (3) removing duplicates and low-quality entries, and (4) creating a clean dataset for training a classification model. Which combination of tools best handles this preprocessing pipeline?
Why D is correct
Dataflow with Apache Beam is the most scalable and efficient approach for this multi-stage pipeline. It handles translation via Translate API, NLP for sentiment/topic extraction, deduplication, and filtering in a single distributed system, then writes clean data to BigQuery. Option D is the only approach that integrates all preprocessing steps in a scalable pipeline. Option B covers translation and deduplication but has no step for sentiment and topic extraction. Option C (Workbench) is not scalable for 1 TB. Option A (Cloud Run) is inefficient for batch processing and adds unnecessary Firestore writes.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (D) is the only one that fully meets every constraint stated in the question.
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (D) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (D) is the only one that fully meets every constraint stated in the question.
Key idea: Translate API & Preprocessing
Dataflow with Apache Beam is the most scalable and efficient approach for this multi-stage pipeline. It handles translation via Translate API, NLP for sentiment/topic extraction, deduplication, and filtering in a single distributed system, then writes clean data to BigQuery. Option D is the only approach that integrates all preprocessing steps in a scalable pipeline. Option B covers translation and deduplication but has no step for sentiment and topic extraction. Option C (Workbench) is not scalable for 1 TB. Option A (Cloud Run) is inefficient for batch processing and adds unnecessary Firestore writes. On the PMLE exam, questions in the "Collaborating to manage data and models" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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