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

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A) Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
B) The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
C) Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
D) Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
E) Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

2. A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A) overwriteSchema
B) partitionBy
C) mergeSchema
D) checkpointLocation

3. A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A) Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
B) Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
C) Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
D) The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
E) Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.

4. Review the following error traceback:

Which statement describes the error being raised?

A) There is a type error because a column object cannot be multiplied.
B) There is no column in the table named heartrateheartrateheartrate
C) The code executed was PvSoark but was executed in a Scala notebook.
D) There is a type error because a DataFrame object cannot be multiplied.
E) There is a syntax error because the heartrate column is not correctly identified as a column.

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: