Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional

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Databricks Certified Data Engineer Professional Sample Questions:

1. 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) 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.
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) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
D) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
E) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.

2. The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A) Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
B) Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
C) Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
D) Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
E) Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.

3. In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A)

B)

C)

D)

E)

4. Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A)

B)

C)

D)

5. A data engineer is performing a join operation to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A) streamingDF.join(userLookup, ["userid"], how="inner")
B) userLookup.join(streamingDF, ["user_id"], how="right")
C) userLookup.join(streamingDF, ["userid"], how="inner")
D) streamingDF.join(userLookup, ["user_id"], how="left")
E) streamingDF.join(userLookup, ["user_id"], how="outer")

Solutions: