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Databricks Certified Data Engineer Professional Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen

Frage #1

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

A. Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
B. Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
C. Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
D. Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.
E. Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

Frage #2

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 overall size of data in the table
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 amount of data in each partition

Frage #3

A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?

A. Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
B. Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
C. Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
D. Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.

Frage #4

Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A. Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
B. Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
C. Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
D. Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.

Frage #5

A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
B. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
C. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
D. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
E. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

Fragen und Antworten: