Databricks Databricks Certification Associate-Developer-Apache-Spark-3.5

Associate-Developer-Apache-Spark-3.5자격증시험은 전문적인 관련지식을 테스트하는 인증시험입니다.은 여러분이 Associate-Developer-Apache-Spark-3.5 시험을 통과할수 있도록 도와주는 사이트입니다. 많은 분들이 많은 시간과 돈을 들여 학원까지 다니면서 Databricks Associate-Developer-Apache-Spark-3.5시험패스에 최선을 다합니다. 하지만 Associate-Developer-Apache-Spark-3.5덤프를 구매하시면 20시간 좌우만 투자하면 무조건 Associate-Developer-Apache-Spark-3.5시험을 패스할 수 있도록 도와드립니다.

Databricks Associate-Developer-Apache-Spark-3.5 시험 요강 주제:

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

문제 #1
6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A. Executors
B. CPU Cores
C. Worker Nodes
D. Driver Nodes

문제 #2
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
B. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
C. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

문제 #3
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
B. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
C. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
D. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

문제 #4
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A. users.write.saveAsTable("default_table").option("path", "/some/path")
B. users.write.option("path", "/some/path").saveAsTable("default_table")
C. users.write.saveAsTable("default_table", path="/some/path")
D. users.write(path="/some/path").saveAsTable("default_table")

문제 #5
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A. It increases the partition size for df1 and df2.
B. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C. It ensures that the join happens only when the id values are identical.
D. It filters the id values before performing the join.

질문과 대답: