Databricks-Certified-Data-Engineer-Associate Exam Dumps : Databricks Certified Data Engineer Associate Exam

A data engineer needs to optimize the data layout and query performance for an e-commerce transactions Delta table. The table is partitioned by " purchase_date " a date column which helps with time-based queries but does not optimize searches on user statistics " customer_id " , a high-cardinality column.

The table is usually queried with filters on " customer_i

d " within specific date ranges, but since this data is spread across multiple files in each partition, it results in full partition scans and increased runtime and costs.

How should the data engineer optimize the Data Layout for efficient reads?

A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricksto ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create a DLT pipeline that reads the rawJSON data and writes it into a Delta table for further processing.

Which code snippet will correctly ingest the raw JSON data and create a Delta table using DLT?

A)

B)

C)

D)

Which compute option should be chosen in a scenario where small-scale ad hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?