The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

• A.Delta Lake statistics are not optimized for free text fields with high cardinality.
• B.Text data cannot be stored with Delta Lake.
• C.ZORDER ON review will need to be run to see performance gains.
• D.The Delta log creates a term matrix for free text fields to support selective filtering.
• E.Delta Lake statistics are only collected on the first 4 columns in a table.

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What is true for Delta Lake?

• A.Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
• B.Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters.
• C.Z-ORDER can only be applied to numeric values stored in Delta Lake tables.
• D.Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

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A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.

• A.withWatermark("event_time", "10 minutes")
• B.awaitArrival("event_time", "10 minutes")
• C.await("event_time + '10 minutes'")
• D.slidingWindow("event_time", "10 minutes")
• E.delayWrite("event_time", "10 minutes")

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A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

• A.Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
• B.Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
• C.Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
• D.Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
• E.Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

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Review the following error traceback:

Which statement describes the error being raised?

• A.The code executed was PvSoark but was executed in a Scala notebook.
• B.There is no column in the table named heartrateheartrateheartrate
• C.There is a type error because a column object cannot be multiplied.
• 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.

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