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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

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

2. 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) 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.
B) Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
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) 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.
E) 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.

3. Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A) spark.sql.adaptive.coalescePartitions.minPartitionNum
B) spark.sql.files.maxPartitionBytes
C) spark.sql.adaptive.advisoryPartitionSizeInBytes
D) spark.sql.autoBroadcastJoinThreshold
E) spark.sql.files.openCostInBytes

4. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A) Can edit
B) Can run
C) Can manage
D) Can Read

5. A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
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A) Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
B) Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
C) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
D) Parse the Delta Lake transaction log to identify all newly written data files.

質問と回答：