Practice Test 4 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
Your company is building storage for data pipelines. The input files are in JSON, the schema for these JSON files can occasionally change. Once data is ingested into the Google cloud, the data scientist of the company will be querying this data using ANSI SQL.
How should the data be ingested considering the above needs?
A. Use BigQuery for storage. Select “Automatically Detect” in the schema section.
B. Use cloud storage for storage. Create Big Query temporary external data source tables and turn on the “Automatically Detect” option in the schema section of Big Query.
C. Use cloud storage for storage. Create permanent external data source tables and turn on the “Automatically Detect” option in the schema section of Big Query.
D. Use BigQuery for storage. Provide format files for data load. Update the format files as needed.
The correct answer is A
Refer GCP documentation – BigQuery Auto Detection:- https://cloud.google.com/bigquery/docs/schema-detect;
Schema auto-detection is available when you load data into BigQuery, and when you query an external data source.
When auto-detection is enabled, BigQuery starts the inference process by selecting a random file in the data source and scanning up to 100 rows of data to use as a representative sample. BigQuery then examines each field and attempts to assign a data type to that field based on the values in the sample.
Option A is correct. As the requirement is to use ANSI SQL for querying purposes and to support occasional schema-changing JSON files; BigQuery will be an appropriate choice. Schema auto-detection is available in BigQuery when you load the data in BigQuery or if your query is from an external data source.
Option B & C is incorrect. Using cloud storage for storage is not ideal for this purpose as it would add latency and would be cumbersome.
Option D is incorrect. Providing format files is not correct, we can simply turn on the ‘Automatically Detect’ schema changes flag.
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