Databricks-DE-Assoc Data Transformation and Modeling 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 json_tuple function to extract multiple fields in one pass, specifying the field names as arguments, and then write the results to a new Delta table.

  Why it's wrong here

  json_tuple can extract multiple fields in one pass, but it returns them as a tuple of strings, without type conversion or schema enforcement. This means all extracted values are strings, requiring additional casting and validation to ensure correct types, which adds complexity and potential for errors. It also does not handle nested JSON as gracefully as from_json with a schema. Thus, it is less efficient for maintaining data quality compared to a schema-based approach.

• ✓

  Use the from_json function with a predefined schema to parse the JSON strings, then select the desired fields and write them to a new Delta table.

  Why this is correct

  Using from_json with a predefined schema is the most efficient and reliable way to parse JSON strings in Spark. It validates the JSON against the schema, handles errors gracefully, and allows you to extract fields directly. This approach maintains data quality by ensuring that the parsed data conforms to expected types and structures, and it can be performed in a single transformation step, making it ideal for this scenario.

• ✗

  Use the get_json_object function for each field to extract values from the raw_data column, then create a new DataFrame with these columns.

  Why it's wrong here

  get_json_object is useful for extracting a single field from a JSON string, but it requires multiple passes over the data if you need several fields, which is inefficient. It also does not enforce a schema, so type consistency is not guaranteed, and errors are not handled as robustly. For extracting multiple fields, from_json with a schema is more efficient and maintainable, providing better data quality controls.

• ✗

  Use the schema_of_json function to infer the schema from a sample of the raw_data, then apply from_json with the inferred schema to parse the JSON.

  Why it's wrong here

  While schema_of_json can infer a schema from a sample, it may not be accurate for all data, especially if the sample is not representative. This can lead to parsing errors or incorrect data types in the new columns, compromising data quality. In a production scenario, it is better to define an explicit schema based on known data requirements to ensure consistency and reliability, rather than relying on inference from a sample.