A team reads a Parquet file that was written with a schema that includes a column of type `Decimal(10, 2)`. They want to ensure that the column is read with the correct type and precision. Which approach should they use?

Use `spark.read.parquet(path)` and rely on schema inference.

Use `spark.read.schema(definedSchema).parquet(path)` where `definedSchema` declares the column as `DecimalType(10, 2)`.

Use `spark.read.option("inferSchema", "true").parquet(path)`.

Use `spark.read.option("mergeSchema", "true").parquet(path)`.

Which statement about schema inference when reading data into a Spark DataFrame is true?

Schema inference can be unreliable when data contains nulls or mixed types.

Schema inference is only available for CSV and JSON files.

Schema inference is always faster than providing an explicit schema.

Schema inference always produces the most accurate data types for downstream analysis.

DatabricksCertified Associate Developer for Apache Spark

Domain 3Objective 3

Input/Output Operations ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Part of the Developing Apache Spark™ DataFrame/DataSet API Applications domain, which accounts for 30% of the ASSOCIATE-DEVELOPER-APACHE-SPARK exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~11–18 in this domain), expect 2–4 from this objective — we provide 9 practice questions to prepare you well beyond it. (estimate)

9questions here

2free pages

3concepts

30%of the exam

Questions 1–5

1application · easy
A team writes a DataFrame to a Delta table. They want to ensure that if the table already exists, the write operation does not fail but also does not overwrite the existing data. Which write mode should they use?
Select an answer first
2application · medium
A team reads a Parquet file that was written with a schema that includes a column of type `Decimal(10, 2)`. They want to ensure that the column is read with the correct type and precision. Which approach should they use?
Select an answer first
3foundation · medium
A data engineer needs to replace the contents of an existing Parquet directory with new data from a DataFrame. Which write mode should they use?
Select an answer first
4expert · hard
A team writes a DataFrame to a Parquet table. They need to ensure that the write operation is idempotent, meaning that running the same job multiple times produces the same result without errors. Which write mode should they use?
Select an answer first
5foundation · medium
Which statement about schema inference when reading data into a Spark DataFrame is true?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to ASSOCIATE-DEVELOPER-APACHE-SPARK

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Databricks. “ASSOCIATE-DEVELOPER-APACHE-SPARK” is a trademark of its owner, used for identification only.

Input/Output Operations ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Questions 1–5

A team writes a DataFrame to a Delta table. They want to ensure that if the table already exists, the write operation does not fail but also does not overwrite the existing data. Which write mode should they use?

A team reads a Parquet file that was written with a schema that includes a column of type `Decimal(10, 2)`. They want to ensure that the column is read with the correct type and precision. Which approach should they use?

A data engineer needs to replace the contents of an existing Parquet directory with new data from a DataFrame. Which write mode should they use?

A team writes a DataFrame to a Parquet table. They need to ensure that the write operation is idempotent, meaning that running the same job multiple times produces the same result without errors. Which write mode should they use?

Which statement about schema inference when reading data into a Spark DataFrame is true?

Keep studying — Developing Apache Spark™ DataFrame/DataSet API Applications