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1. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A) All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
B) All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
C) All records from all columns will be displayed with the values in user_ltv.
D) All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
E) All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.

2. A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A) Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
B) The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
C) Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
D) The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
E) Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

3. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A) Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
B) Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C) Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D) All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E) Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

4. Which statement describes Delta Lake Auto Compaction?

A) Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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B) Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
C) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
D) Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
E) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.

5. Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A) Query's detail screen and Job's detail screen
B) Driver's and Executor's log files
C) Executor's detail screen and Executor's log files
D) Stage's detail screen and Query's detail screen
E) Stage's detail screen and Executor's log files

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