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Databricks Certified Data Engineer Professional Sample Questions:

1. A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot. How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A) Set the pipelines, reset, allowed property to false on bpm_stats
B) Set the SkipChangeCommits flag to true raw_lot
C) Set the pipelines, reset, allowed property to false on raw_iot
D) Set the skipChangeCommits flag to true on bpm_stats

2. The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A) Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
B) Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
D) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
E) Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.

3. A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
B) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
C) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
D) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
E) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

4. Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A) In the Query Detail screen, by interpreting the Physical Plan
B) In the Delta Lake transaction log. by noting the column statistics
C) In the Executor's log file, by gripping for "predicate push-down"
D) In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
E) In the Storage Detail screen, by noting which RDDs are not stored on disk

5. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B) Databricks has autotuned to a smaller target file size based on the amount of data in each partition
C) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D) Databricks has autotuned to a smaller target file size based on the overall size of data in the table

Solutions: