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問題 #1

38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
B. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');
C. CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');
D. CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');

問題 #2

21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A. The same start date will be returned.
B. The number of days specified will be removed from the start date.
C. An error message of an invalid parameter will be returned.
D. The number of days specified will be added to the start date.

問題 #3

A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
C. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
D. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs

問題 #4

24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A. spark.read.parquet("events.parquet").printSchema()
B. spark.sql("SELECT * FROM events.parquet").show()
C. spark.read.format("parquet").load("events.parquet").show()
D. spark.sql("SELECT schema FROM events.parquet").show()

問題 #5

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

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