Google Cloud Professional Data Engineer Exam

What table partitioning type is most suitable for ingesting data spread over a wide range of dates into BigQuery?

1. Create an ingestion-time partitioned table with daily partitioning type.

2. Create an ingestion-time partitioned table with yearly partitioning type.

3. Create an integer-range partitioned table.

4. Create a time-unit column-partitioned table with yearly partitioning type.

The correct answer is: Create an ingestion-time partitioned table with daily partitioning type.

The most suitable choice for ingesting data spread over a wide range of dates into BigQuery is to create an ingestion-time partitioned table with daily partitioning. This is particularly effective because it allows for finer granularity in data management and querying over a wide span of dates. Daily partitioning means that each day's data is stored in its own partition. This structure effectively organizes the data, making it easier to query specific time ranges since partitions can be accessed independently. When data comes in continuously over a wide date range, daily partitions facilitate more efficient querying and processing, particularly if users are often interested in recent or specific date ranges. By using ingestion-time partitioning, BigQuery automatically assigns a partition to each incoming record based on the time the data is ingested. This is advantageous when dealing with streaming data or frequent batch uploads, as you don’t need to manage the partitioning explicitly based on designated timestamps, simplifying the data ingestion process. Other partitioning types may not offer the same benefits for this scenario. For instance, yearly partitioning might not capture the granularity needed for querying daily variations in data, and integer-range partitioning is less relevant when dealing specifically with date data. Time-unit column-partitioned tables are useful but may involve more