Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
A banking system is linked to over 400 ATM machines distributed around a region. Each ATM machine sends event data about machine’s current state (active, standby, maintenance, ..), bank note balance, current activity type (withdrawal, check balance, ..) and other stats related to business and security purposes. Due to the dynamic attribute structure sent by ATM machines, JSON-formatted events are sent to the centralized system.
The head office’s chief data officer wants event data received from ATM machines to be stored in a data warehouse after required cleansing and transformation for the analytics team to fetch reports using SQL-syntax. Which of the following is the best to achieve this?
A. Store event data to Google Storage after converting data to ORC format. Launch a Dataproc cluster to create external tables using Apache Hive on data residing in Google Storage.
B. Load the data to BigQuery with enabling the “auto-detect” option.
C. Import the data to BigTable. Choose a key combination which allows the best performance while fetching event data based on Google’s recommendations.
D. Build a Dataflow pipeline to read JSON data and transform it into a structured format like CSV. Then, load the data to BigQuery.
Correct Answer: B
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 the best-effort attempt to automatically infer the schema for CSV and JSON files.
Source(s):
BigQuery – Auto-detect schema:
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