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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A) Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.
B) Define ten Notebook tasks to clearly track which report download failed.
C) Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
D) Use a foreach task with a list of report types as its inputs.

2. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
B) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
C) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
D) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
E) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.

3. The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A) The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
B) The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
C) An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
D) An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
E) A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.

4. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) The number of Cached Partitions> the number of Spark Partitions
B) On Heap Memory Usage is within 75% of off Heap Memory usage
C) The RDD Block Name included the '' annotation signaling failure to cache
D) Size on Disk is> 0
E) Size on Disk is < Size in Memory

5. A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A) spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
B) spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")
C) spark.sql("VACUUM my_table RETAIN 15 HOURS")
D) from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"

Solutions: