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Databricks Associate Developer for Apache Spark Exam Practice Questions, practice question pack
DATABRICKS · Associate Developer for Apache Spark

Databricks Associate Developer for Apache Spark Exam Practice Questions

99% pass rateUpdated 1 Nov 2026342 questions2 PDFs by email

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Every question with the correct answer and why the other options are wrong.

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Three of the 342 questions in this pack

Question 1

Which of the following operations can be used to create a DataFrame with a subset of columns from DataFrame storesDF that are specified by name?

  1. storesDF.subset()
  2. storesDF.select()
  3. storesDF.selectColumn()
  4. storesDF.filter()
  5. storesDF.drop()
Show answer and explanation

Correct answer: B. storesDF.select()

The select() method is the standard PySpark operation for creating a DataFrame containing a subset of columns specified by name. It accepts column names as arguments and returns a new DataFrame with only those columns.

Why the other options are wrong

  • A. subset() is not a valid DataFrame method in PySpark.
  • C. selectColumn() does not exist in the PySpark API.
  • D. filter() is used to subset rows based on conditions, not to select columns.
  • E. drop() removes columns rather than selecting specific ones.

Question 2

Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000?

  1. storesDF.filter("sqft" <= 25000)
  2. storesDF.filter(sqft > 25000)
  3. storesDF.where(storesDF[sqft] > 25000)
  4. storesDF.where(sqft > 25000)
  5. storesDF.filter(col("sqft") <= 25000)
Show answer and explanation

Correct answer: E. storesDF.filter(col("sqft") <= 25000)

The filter() method with col() function is the correct syntax for filtering rows in PySpark. The col() function references a column by name, and col("sqft") <= 25000 creates a proper boolean condition for filtering.

Why the other options are wrong

  • A. Strings cannot be used directly with comparison operators in filter conditions; col() is required.
  • B. Unquoted column names like sqft are treated as undefined variables, not column references.
  • C. The bracket syntax storesDF[sqft] is invalid; where() also requires col() for proper column reference.
  • D. The operator > filters for values greater than, not less than or equal to as required.

Question 3

Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000 OR the value in column customerSatisfaction is greater than or equal to 30?

  1. storesDF.filter(col("sqft") <= 25000 | col("customerSatisfaction") >= 30)
  2. storesDF.filter(col("sqft") <= 25000 or col("customerSatisfaction") >= 30)
  3. storesDF.filter(sqft <= 25000 or customerSatisfaction >= 30)
  4. storesDF.filter(col(sqft) <= 25000 | col(customerSatisfaction) >= 30)
  5. storesDF.filter((col("sqft") <= 25000) | (col("customerSatisfaction") >= 30))
Show answer and explanation

Correct answer: E. storesDF.filter((col("sqft") <= 25000) | (col("customerSatisfaction") >= 30))

The filter() method with col() functions and the pipe operator (|) for OR logic is the correct syntax. Both col("sqft") and col("customerSatisfaction") properly reference columns, and the | operator combines conditions with OR logic. Parentheses ensure clarity in the compound condition.

Why the other options are wrong

  • A. While the syntax is mostly correct, omitting parentheses around the compound condition is less clear and may cause precedence issues.
  • B. Python's "or" keyword is not used in PySpark column expressions; the pipe operator | is required.
  • C. Unquoted column names and the Python "or" keyword are both invalid in PySpark filter conditions.
  • D. The col() function requires quoted strings as arguments; col(sqft) without quotes is invalid syntax.

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How much of the Databricks Associate Developer for Apache Spark bank is free?

342 questions in the bank

Each step includes the one before, so buying only adds new questions.

  • You are here10questionsFree sampleThree on this page, all ten as a PDF.Download the sample
  • 8525% of the bankPractice pageFree on the site, every answer explained.Go to the practice page
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ABOUT THIS PACK

What’s in the Databricks Associate Developer for Apache Spark pack

  • 342 questions mapped to the Associate Developer for Apache Spark exam guide
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Delivered by email shortly after checkout
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

Failing this exam costs another US$200. This pack is US$39, paid once.

What makes the Associate Developer for Apache Spark hard

The exam was rebuilt for Apache Spark 3.5, and the rebuild changed its character. The old version was mostly DataFrame API syntax recall with a small architecture section. The current one adds Structured Streaming, the Pandas API on Spark and Spark Connect, and asks more often why a job behaves the way it does.

It is 45 scored questions in 90 minutes, Python only, and there is no documentation available during the exam, so the API surface has to be in your head. Developing DataFrame applications is the largest section: selecting, filtering, joining, aggregating and sorting, column expressions, handling nulls and dates, user-defined functions, reading and writing Parquet, Delta, JSON and CSV, and the difference between transformations and actions.

Most misses are on lazy evaluation and on which operation triggers a shuffle.

About the exam

The Databricks Certified Associate Developer for Apache Spark validates the ability to use the Spark DataFrame API in Python to complete data manipulation tasks, and an understanding of Spark architecture, Spark SQL, Structured Streaming, the Pandas API on Spark and Spark Connect. There are no prerequisites.

Questions before you buy

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Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack, or swap it for any other pack if you would rather keep studying. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

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99% of customers who prepare with a CertStash pack pass. If you do not, we refund the pack, or swap it for another pack.

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Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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