Associate-Developer-Apache-Spark-3.5 試験問題を無料オンラインアクセス

In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

• A.aggregate
• B.replace
• C.complete
• D.append

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

A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference inevent_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

• A.It is not able to handle deduplication in this scenario
• B.It removes duplicates that arrive within the 30-minute window specified by the watermark
• C.It removes all duplicates regardless of when they arrive
• D.It accepts watermarks in seconds and the code results in an error

A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

• A.Configure the Spark UI to automatically collect maximum times
• B.Broadcast a variable to share the maximum time among workers
• C.Use an RDD action like reduce() to compute the maximum time
• D.Use an accumulator to record the maximum time on the driver

A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

• A.Transformations are executed immediately to build the lineage graph.
• B.Transformations are evaluated lazily.
• C.The Spark engine requires manual intervention to start executing transformations.
• D.The Spark engine optimizes the execution plan during the transformations, causing delays.
• E.Only actions trigger the execution of the transformation pipeline.