A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high-cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time.
Which command should the data engineer implement?

• A.Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
• B.Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
• C.Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
• D.Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.

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Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

• A.* Total VMs; 1
  * 400 GB per Executor
  * 160 Cores / Executor
• B.* Total VMs: 8
  * 50 GB per Executor
  * 20 Cores / Executor
• C.* Total VMs: 4
  * 100 GB per Executor
  * 40 Cores/Executor
• D.* Total VMs:2
  * 200 GB per Executor
  * 80 Cores / Executor

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A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline.
How should the data engineer perform this syntax check?

• A.Use the "Validate" option in the notebook to check for syntax errors.
• B.Open the web terminal from the notebook and run a shell command to validate the pipeline code.
• C.Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
• D.Switch to a workspace file instead of a notebook to access validation and diagnostics tools.

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Which statement regarding stream-static joins and static Delta tables is correct?

• A.Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
• B.Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
• C.The checkpoint directory will be used to track state information for the unique keys present in the join.
• D.Stream-static joins cannot use static Delta tables because of consistency issues.
• E.The checkpoint directory will be used to track updates to the static Delta table.

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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.Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
• C.Databricks has autotuned to a smaller target file size based on the overall size of data in the table
• D.Databricks has autotuned to a smaller target file size based on the amount of data in each partition

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