Exam Associate-Data-Practitioner Topic 2 Question 44 Discussion
Actual exam question for Google's Associate-Data-Practitioner exam
Question #: 44
Topic #: 2
Question #: 44
Topic #: 2
You work for a retail company that collects customer data from various sources:
* Online transactions: Stored in a MySQL database
* Customer feedback: Stored as text files on a company server
* Social media activity: Streamed in real-time from social media platformsYou need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?
* Online transactions: Stored in a MySQL database
* Customer feedback: Stored as text files on a company server
* Social media activity: Streamed in real-time from social media platformsYou need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?
Suggested Answer: B Vote an answer
Comprehensive and Detailed In-Depth Explanation:
The pipeline must extract diverse data types and load them into systems optimized for analysis and ML. Let's assess:
* Option A: Cloud SQL for transactions keeps data relational but isn't ideal for analysis/ML (less scalable than BigQuery). BigQuery for feedback is fine but skips staging. Cloud Storage for streaming social media loses real-time context and requires extra steps for analysis.
* Option B: BigQuery for transactions (via export from MySQL) supports analysis/ML with SQL. Cloud Storage stages feedback text files for preprocessing, then BigQuery ingestion. Pub/Sub and Dataflow stream social media into BigQuery, enabling real-time analysis-optimal for all sources.
* Option C: Cloud Storage for all data is a staging step, not a final solution for analysis/ML, requiring additional pipelines.
The pipeline must extract diverse data types and load them into systems optimized for analysis and ML. Let's assess:
* Option A: Cloud SQL for transactions keeps data relational but isn't ideal for analysis/ML (less scalable than BigQuery). BigQuery for feedback is fine but skips staging. Cloud Storage for streaming social media loses real-time context and requires extra steps for analysis.
* Option B: BigQuery for transactions (via export from MySQL) supports analysis/ML with SQL. Cloud Storage stages feedback text files for preprocessing, then BigQuery ingestion. Pub/Sub and Dataflow stream social media into BigQuery, enabling real-time analysis-optimal for all sources.
* Option C: Cloud Storage for all data is a staging step, not a final solution for analysis/ML, requiring additional pipelines.
by Jessica at Jan 07, 2026, 05:35 AM
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