Google Professional Data Engineer Question 99
Single answerGoogle Cloud PlatformYou are designing a data pipeline that involves processing large amounts of textual data in multiple languages, including English, Spanish, and Chinese. The goal of the pipeline is to extract meaningful insights such as sentiment analysis and entity recognition. Which Google Cloud tool or service should you use to minimize development effort while supporting multilingual capabilities?
- A
Google Cloud Translation API
- B
Cloud Natural Language API
- C
BigQuery ML
- D
Cloud Speech-to-Text API
Show answer and explanation
Correct answer: B
Explanation
The Cloud Natural Language API is the most suitable tool for the described scenario as it provides pre-built capabilities like sentiment analysis and entity recognition while supporting multiple languages. This eliminates the need for custom development and ensures efficient processing of multilingual text data.
- A. Incorrect.
Google Cloud Translation API is primarily used for translating text between different languages. While it supports multilingual text handling, it does not provide built-in capabilities for sentiment analysis or entity recognition.
- B. Correct.
Cloud Natural Language API is designed for analyzing text, including sentiment analysis, entity recognition, syntax analysis, and content classification, and it supports multiple languages. This makes it the best choice for the described scenario.
- C. Incorrect.
BigQuery ML is used for building and deploying machine learning models on structured datasets in BigQuery. While it supports natural language use cases when combined with other tools, it doesn't directly offer out-of-the-box sentiment analysis or entity recognition for multilingual text.
- D. Incorrect.
Cloud Speech-to-Text API is designed for converting audio to text but does not address the specific task of text analysis, such as sentiment analysis or entity recognition.