A data engineer has ingested a JSON file into a table raw_table with the following schema:
1.transaction_id STRING,
2.payload ARRAY<customer_id:STRING, date:TIMESTAMP, store_id:STRING>
The data engineer wants to efficiently extract the date of each transaction into a table with the fol-lowing
schema:
1.transaction_id STRING,
2.date TIMESTAMP
Which of the following commands should the data engineer run to complete this task?

• A.1.SELECT transaction_id, explode(payload)
  2.FROM raw_table;
• B.1.SELECT transaction_id, date
  2.FROM raw_table;
• C.1.SELECT transaction_id, payload.date
  2.FROM raw_table;
• D.1.SELECT transaction_id, date from payload
  2.FROM raw_table;
• E.1.SELECT transaction_id, payload[date]
  2.FROM raw_table;

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Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

• A.Regex
• B.Julia
• C.pyspsark.ml.feature
• D.Scala Datasets
• E.C++

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A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team.
What steps the data engineer needs to follow using built-in tools?

• A.Use the notebook interactive debugger to re-run the entire multi-task job, and capture step-through traces for the failing task.
• B.Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
• C.Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
• D.From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.

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An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

• A.Each write to the orders table willonly contain unique records, and only those records without duplicates in the target table will be written.
• B.Each write to the orders table willonly contain unique records, but newly written records may have duplicates already present in the target table.
• C.Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
• D.Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
• E.Each write to the orders table willrun deduplication over the union of new and existing records, ensuringno duplicate records are present.

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A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

• A.Set the skipChangeCommits flag to true on bpm_stats
• B.Set the SkipChangeCommits flag to true raw_lot
• C.Set the pipelines, reset, allowed property to false on bpm_stats
• D.Set the pipelines, reset, allowed property to false on raw_iot

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