A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

• A. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
• B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
• C. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
• D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users .

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

• A. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
• B. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
• C. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
• D. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
• E. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.

A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source. That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

• A. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
• B. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
• C. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
• D. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
• E. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

• A. Unit
• B. functional
• C. Integration
• D. Manual