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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Skew caused by more data being assigned to a subset of spark-partitions.
B) Network latency due to some cluster nodes being in different regions from the source data
C) Spill resulting from attached volume storage being too small.
D) Task queueing resulting from improper thread pool assignment.
E) Credential validation errors while pulling data from an external system.

2. A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A) Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
B) Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
C) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
D) Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
E) Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

3. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A) Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
B) Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
C) Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
D) Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
E) Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.

4. A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A) Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
B) Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
C) Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
D) Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.

5. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Increase spark.sql.shuffle.partitions from 200 to 10000.
B) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
C) Force a sort-merge join with /*+ MERGE(r) */.
D) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.

Solutions: