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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

2. 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 can be used to interact with any remote cluster using the REST API.
C) It provides a way to run Spark applications remotely in any programming language.
D) It allows for remote execution of Spark jobs.

3. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
B) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
C) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
D) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")

4. 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 ensures that the join happens only when the id values are identical.
C) It filters the id values before performing the join.
D) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.

5. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Optimizing the layout of Delta files on disk
B) Collecting persistent table statistics and storing them in the metastore for future use
C) Dynamically switching join strategies
D) Improving the performance of single-stage Spark jobs

Solutions: