A team needs to read a subset of rows from a large JDBC table into Spark. The table has a 'created_at' column. They want to minimize the amount of data transferred from the database to Spark. Which approach should they use?

Use the query option with a SELECT statement that fetches all columns and rows.

Use the dbtable option with a subquery that includes a WHERE clause to filter the data in the database.

Use the partitionColumn option to split the read, but do not filter.

Read the entire table and then filter with a WHERE clause in Spark.

A team reads a large CSV file into Spark. The file has a header row and a mix of numeric and string columns. They want to ensure that the data types are correctly inferred and that the header is not treated as data. Which options should they set?

header=true and mergeSchema=true

inferSchema=true and mode=PERMISSIVE

header=true and inferSchema=true

DatabricksCertified Associate Developer for Apache Spark

Domain 2Objective 1

Utilize Common Data Sources Such as JDBC, Files, Etc., to Efficiently Read from and Write to Spark DataFrames Using Spark SQL, Including Overwriting and Partitioning by Column. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

Part of the Using Spark SQL domain, which accounts for 20% of the ASSOCIATE-DEVELOPER-APACHE-SPARK exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~7–12 in this domain), expect 2–3 from this objective — we provide 20 practice questions to prepare you well beyond it. (estimate)

20questions here

4free pages

7concepts

20%of the exam

Questions 6–10

6foundation · easy
Which Spark SQL option is used when writing a CSV file to include the header row in the output?
Select an answer first
7foundation · easy
Which Spark SQL write mode will create a new table in a JDBC source if it does not exist and fail if the table already exists?
Select an answer first
8application · medium
A team needs to read a subset of rows from a large JDBC table into Spark. The table has a 'created_at' column. They want to minimize the amount of data transferred from the database to Spark. Which approach should they use?
Select an answer first
9foundation · easy
Which file format is generally recommended for efficient storage and query performance in Spark?
Select an answer first
10application · medium
A team reads a large CSV file into Spark. The file has a header row and a mix of numeric and string columns. They want to ensure that the data types are correctly inferred and that the header is not treated as data. Which options should they set?
Select an answer first

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Utilize Common Data Sources Such as JDBC, Files, Etc., to Efficiently Read from and Write to Spark DataFrames Using Spark SQL, Including Overwriting and Partitioning by Column. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

Questions 6–10

Which Spark SQL option is used when writing a CSV file to include the header row in the output?

Which Spark SQL write mode will create a new table in a JDBC source if it does not exist and fail if the table already exists?

A team needs to read a subset of rows from a large JDBC table into Spark. The table has a 'created_at' column. They want to minimize the amount of data transferred from the database to Spark. Which approach should they use?

Which file format is generally recommended for efficient storage and query performance in Spark?

A team reads a large CSV file into Spark. The file has a header row and a mix of numeric and string columns. They want to ensure that the data types are correctly inferred and that the header is not treated as data. Which options should they set?

Keep studying — Using Spark SQL