A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

• A.Federation provides read-only access that reflects the current state of source systems.
• B.Federation implements change data capture (CDC) from all sources.
• C.A separate data synchronization service must be deployed.
• D.Federation creates local copies that must be manually refreshed.

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A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

• A.
• B.
• C.
• D.

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A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
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Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

• A.Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
• B.Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
• C.Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
• D.Both commands will fail. No new variables, tables, or views will be created.
• E.Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

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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 data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

• A.Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
• B.COPY INTO SQL command with parameters to track processed files
• C.Traditional batch processing with scheduled Databricks Jobs
• D.Auto Loader with schema inference and evolution enabled

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