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최신 Databricks Certification Associate-Developer-Apache-Spark-3.5 무료샘플문제:

1. A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

A) employees_df.where(employees_df.tenure >= 5)
B) filter(employees_df.tenure >= 5)
C) employees_df.filter(employees_df.tenure >= 5).collect()
D) employees_df.filter(employees_df.tenure >= 5).show()

2. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the executor memory allocation in the Spark configuration.
B) Set the spark.network.timeout property to allow tasks more time to complete without being killed.
C) Reduce the size of the data partitions to improve task scheduling.
D) Increase the number of executor instances to handle more concurrent tasks.

3. 45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A) It is primarily used for data ingestion into Spark from external sources.
B) It provides a way to run Spark applications remotely in any programming language.
C) It allows for remote execution of Spark jobs.
D) It can be used to interact with any remote cluster using the REST API.

4. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
B) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
C) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
D) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.

5. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("overwrite").parquet("/data/output")
B) df.write.partitionBy("country").parquet("/data/output")
C) df.write.mode("append").partitionBy("country").parquet("/data/output")
D) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")

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