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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Save the users_vw definition and query using Spark
B) Recreate the users_vw and query the data using Spark
C) Query the users_vw using Spark
D) Persist the users_vw data as a table

2. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A shuffled hash join
B) A broadcast nested loop join
C) A Cartesian join
D) A sort-merge join

3. Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A) dropDuplicates on all columns (wrong criteria)
B) dropDuplicates with no arguments (removes based on all columns)
C) dropDuplicates on the exact matching fields
D) groupBy without aggregation (invalid use)

4. Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B) The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
C) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
D) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.

5. 54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
B) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
C) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
D) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.

Solutions: