Google Professional Data Engineer Question 100
Select 2Google Cloud PlatformYou are working on a natural language processing (NLP) project to analyze customer feedback and categorize it into predefined sentiment categories: Positive, Negative, and Neutral. Which Google Cloud tools or APIs should you use to implement this solution efficiently?
- A
Cloud Natural Language API
- B
Cloud Translation API
- C
BigQuery ML
- D
TensorFlow
- E
Cloud Speech-to-Text
Show answer and explanation
Correct answers: A, C
Explanation
The Cloud Natural Language API provides a ready-to-use solution for sentiment analysis, making it highly efficient for the task. BigQuery ML is also suitable for building a custom model if you have structured text data and require advanced customization. While other options like TensorFlow or Cloud Speech-to-Text could be part of a broader solution, they are not directly tailored for sentiment analysis in this scenario.
- A. Correct.
Cloud Natural Language API is designed for NLP tasks, such as sentiment analysis, entity recognition, and syntax analysis, making it a perfect fit for analyzing customer feedback.
- B. Incorrect.
Cloud Translation API is used for translating text between different languages. It is not directly relevant for sentiment analysis unless translation is required before analysis.
- C. Correct.
BigQuery ML can be used to build custom machine learning models, including those for sentiment classification, by leveraging structured data available in BigQuery.
- D. Incorrect.
TensorFlow is a general-purpose machine learning framework. While you could build a sentiment analysis model using TensorFlow, it would require significant time and effort compared to using pre-built tools like Cloud Natural Language API or BigQuery ML.
- E. Incorrect.
Cloud Speech-to-Text is used to transcribe audio into text. While useful for applications involving spoken feedback, it is not directly related to the sentiment analysis of text.