Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
A weather forecasting facility receives events from its 25,000 sensors every 10 seconds. Those events are stored in Google Storage in JSON format. Events can have different attributes based on purpose, location and brand. Data Science team wants to apply their SQL-queries on this data for further transformation and forecasting analysis.
Which of the following approaches is best to satisfy Data Scientists request?
A. Load the data directly to BigQuery with enabling “auto-detect” option.
B. Build a dataflow pipeline to read JSON data and transform it to a structured format like CSV. Then, load the data to BigQuery.
C. Import the data to BigTable. Choose combination #eventType-location-brand to differentiate between different events.
D. Use Dataproc cluster and create Hive external clusters on the data for data scientists to query data.
Answer: A.
Schema auto-detection: 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 the 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. BigQuery makes a best-effort attempt to automatically infer the schema for CSV and JSON files.
So, answer A is the correct answer.
The other answers are complicated and unnecessary approaches for this scenario.
Source(s):
BigQuery – Auto-detect schema: https://cloud.google.com/bigquery/docs/schema-detect
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