DEA-C01 Data Ingestion and Transformation Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Use the 'Relationalize' transform.

  Why this is correct

  The Relationalize transform in AWS Glue flattens nested JSON structures into a relational schema. It unnestes arrays and structs, producing multiple tables that can be joined. This is specifically designed for flattening nested data. It is efficient and scalable because it leverages Spark. The transform outputs a DynamicFrame collection, which can then be written to Parquet. This is the correct choice for flattening nested JSON.

• ✗

  Use the 'ApplyMapping' transform.

  Why it's wrong here

  ApplyMapping is used to rename, cast, and select fields, but it does not flatten nested structures. It can handle nested fields by specifying paths, but it does not unnest arrays or structs into separate rows or tables. For flattening, you need a transform that can explode arrays and create separate relations. ApplyMapping alone would not achieve the required flattening.

• ✗

  Use the 'Filter' transform.

  Why it's wrong here

  The Filter transform is used to select a subset of records based on a condition. It does not alter the structure of the data or flatten nested fields. It is useful for removing unwanted data but not for restructuring nested JSON. Using Filter would not flatten the data; it would only reduce the number of records.

• ✗

  Use the 'Join' transform.

  Why it's wrong here

  The Join transform combines two DynamicFrames based on a key. It does not flatten nested structures within a single DynamicFrame. Joining would require multiple frames, but the goal is to flatten a single nested frame. Join is not appropriate for unnesting arrays or structs. It is used for combining data from different sources, not for flattening.

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