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Databricks Associate Developer for Apache Spark — Practice Questions and Mock Exam

Prepare for DB-SPARK with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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45Mock exam questions

90minTime limit

Checked against Databricks · August 2026 · Current exam version

Overview

About the exam

The Databricks Certified Associate Developer for Apache Spark exam validates the ability to build applications using Apache Spark. It covers the DataFrame and Dataset APIs, Spark SQL, Spark architecture and components, Structured Streaming, and troubleshooting and tuning DataFrame applications. Candidates must demonstrate proficiency in Spark Connect and the Pandas API on Spark; the current exam guide frames every task in Python.

This certification is designed for developers with six or more months of experience building applications with Apache Spark using Python or Scala. It demonstrates practical fluency in Spark's core APIs and the ability to reason about performance and troubleshooting in a distributed compute environment.

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Try five DB-SPARK questions

Try five practice questions from the app’s current Databricks Certified Associate Developer for Apache Spark question bank, with answers and explanations.

Apache Spark Architecture and Components1 / 5

A Spark executor has 4 cores and 16 GB of memory. How many tasks can run concurrently on this executor?

AlexFull explanation from Alex

Concurrent tasks per executor = spark.executor.cores / spark.task.cpus. With the default spark.task.cpus=1, each core runs one task, so 4 cores = 4 concurrent tasks. Increasing spark.task.cpus reduces concurrency but gives each task more CPU—useful for multi-threaded ML algorithms or GPU workloads. Memory is shared: each task's working memory ≈ (executor memory × memory fraction) / concurrent tasks. Distractor analysis: The option “The number of concurrent tasks is capped…” is wrong—spark.default.parallelism controls total partitions, not per-executor concurrency. The option “Concurrency is limited by…” is wrong—spark.scheduler.maxRegisteredResourcesWaitingTime is unrelated to task slot count. The option “Up to 4 tasks can run concurrently…” is wrong—memoryFraction affects spill thresholds but doesn't cap task concurrency. Ref: spark.apache.org/docs/latest/configuration.html#scheduling

Sourcedatabricks.com

Troubleshooting and Tuning Apache Spark DataFrame API Applications2 / 5

A developer notices that a Spark job has one task in a shuffle stage that takes 10 minutes while all other tasks complete in 30 seconds. What is this problem called and how should it be addressed?

AlexFull explanation from Alex

Data skew is a common Spark performance problem where uneven data distribution causes stragglers. Symptoms: one or few tasks take much longer than the rest in the Spark UI. Solutions: (1) AQE skew join (spark.sql.adaptive.skewJoin.enabled=true, default in Spark 3.x) automatically splits oversized partitions, (2) key salting appends a random number to the skewed key and replicates the smaller side of the join, (3) broadcast join if the non-skewed table is small enough, (4) pre-aggregation to reduce data volume before the skewed operation.

Sourcespark.apache.org

Using Pandas API on Spark3 / 5

When using the pandas API on Spark, a developer writes ps_df.to_pandas(). What happens?

AlexFull explanation from Alex

to_pandas() is the escape hatch from distributed to local processing. It uses Apache Arrow for efficient serialization (columnar format, zero-copy when possible). The data flow: executors → driver (over network) → Arrow batches → pandas DataFrame. Size considerations: the driver needs enough memory to hold the entire DataFrame in pandas format, which is typically 2-10x the Spark in-memory size due to Python object overhead. Safety pattern: if len(ps_df) > threshold: ps_df = ps_df.sample(frac=threshold/len(ps_df)); local_df = ps_df.to_pandas().

Sourcespark.apache.org

Structured Streaming4 / 5

A developer calls spark.streams.awaitAnyTermination(timeout=3600). What does this do?

AlexFull explanation from Alex

Production streaming application lifecycle: (1) start streaming queries with writeStream.start(), (2) call spark.streams.awaitAnyTermination() to block the main thread. Without blocking, the driver process exits and all streaming queries stop. awaitAnyTermination waits for ANY query to stop (on error or explicit stop()). After it returns, check for exceptions: query.exception shows failure details. For long-running production apps, use awaitAnyTermination() without timeout (blocks indefinitely). In notebooks, this is not needed because the notebook kernel keeps the session alive.

Sourcespark.apache.org

Developing Apache Spark™ DataFrame/DataSet API Applications5 / 5

A developer writes df.alias('orders').join(df2.alias('items'), col('orders.order_id') == col('items.order_id')). What does alias() do on a DataFrame?

AlexFull explanation from Alex

DataFrame.alias() assigns a logical name to a DataFrame for qualifying column references in joins and selections. This is essential for self-joins (df.alias('current').join(df.alias('previous'), ...)) and any join where both DataFrames share column names. The alias adds a qualifier for column resolution—it does not copy data or modify the DataFrame. In SQL, this is equivalent to FROM orders AS o. Distractor analysis: The option “Creates a logical scope that isolates the DataFrame's…” is wrong—alias does not create isolation scopes or block optimizations. The option “Declares a reusable DataFrame identifier…” is wrong—alias does not register anything in the SparkSession catalog. The option “Registers a named reference in the query plan's symbol table…” is wrong—alias is simpler than symbol table registration; it's just a column name qualifier. Ref: docs.databricks.com/en/pyspark/reference/classes/dataframe/alias.html

Sourcedatabricks.com

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Question pool

311 practice questions

Use the Pass-IT question pool to practice for DB-SPARK. Mock exams are set to 45 questions in 90 minutes.

Pool details: DB-SPARK

Objectives in the guide32 objectives listed in the official guide

across 7 domains in the official exam guide

Pool size311 questions

= The pool size is equivalent to 6 sets of 45 questions; this does not mean that each mock exam uses a separate set.

Recorded as checked against sources311 of 311

questions recorded as having their answer, options, and explanation checked against official Databricks documentation

How questions are made →

Exam domains

What's on the exam

DataFrame and Dataset API development is the largest domain at 30%, covering column and row manipulation, joins, aggregations, and user-defined functions with stateful operators. Architecture and components (20%) and Spark SQL (20%) follow closely, together worth as much as the DataFrame domain alone — testing the driver-executor model, partitioning and shuffles, lazy evaluation, and reading or writing through Spark SQL against JDBC and file sources.

Troubleshooting and tuning and Structured Streaming each carry 10%, covering Adaptive Query Execution and micro-batch processing with exactly-once semantics. Spark Connect and Spark's Pandas API are the lightest domains at 5% apiece — newer or narrower topics that many study guides skip but that still appear on the exam.

Exam blueprint: DB-SPARK

Developing Apache Spark™ DataFrame/DataSet API Applications30%

Manipulate DataFrame columns, rows, and structures through filtering, joining, aggregating, and date operations, and manage I/O operations and user-defined functions including stateful operators. Also covers broadcast variables, accumulators, and the purpose of broadcast joins.

≈ 24 h

Apache Spark Architecture and Components20%

Identify the advantages and core architectural components of Apache Spark, including the driver, executors, and SparkSession lifecycle, and explain Spark's execution hierarchy, partitioning, and lazy evaluation model. Also covers the features of Spark modules such as Spark SQL, Structured Streaming, and MLlib.

≈ 16 h

Using Spark SQL20%

Read and write DataFrames against sources like JDBC and flat files, query files directly in formats such as ORC, JSON, and Delta, and register a DataFrame as a temporary view so it can be queried with plain SQL.

≈ 16 h

Troubleshooting and Tuning Apache Spark DataFrame API Applications10%

Tune Spark performance by repartitioning or coalescing data to fight skew and cut shuffling, understand what Adaptive Query Execution does for you automatically, and read driver and executor logs to catch out-of-memory errors and underused clusters.

≈ 8 h

Structured Streaming10%

Learn how Spark's Structured Streaming engine processes data in micro-batches with fault-tolerant, exactly-once guarantees, then build streaming DataFrames that select, window, aggregate, and de-duplicate records as they arrive.

≈ 8 h

Using Spark Connect to deploy applications5%

Learn what Spark Connect enables for remote application development, and compare it against Apache Spark's client, cluster, and local deployment modes.

≈ 4 h

Using Pandas API on Spark5%

See what the Pandas API on Spark buys you over plain Pandas, and write Pandas UDFs that run against that same interface.

≈ 4 h

Format

Exam format and question types

The exam consists of 45 scored, multiple-choice questions inside a 90-minute window, with unscored pilot items possible. Questions run scenario-based across the DataFrame and Dataset APIs, Spark SQL, and cluster architecture, testing whether a given operation is lazy or eager and how a partitioning choice affects downstream performance rather than syntax alone.

Question types: DB-SPARK

Multiple Choice100%

Select the single answer that best meets the question’s requirements.

See Databricks for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for DB-SPARK

The exam is delivered online with a remote proctor or at a test center, offered in English only. The credential holds for two years, and recertifying means passing the current version of the exam.

Preparation and logistics: DB-SPARK

Preparation

Illustrative study time50–120 h

illustrative planning range: 50 h with relevant experience to 120 h when starting out; your needs may fall outside this range

LevelAssociate

Recommended backgroundNone required. 6+ months hands-on Apache Spark development experience recommended.

Taking and maintaining the certification

DeliveryOnline proctored or test center

Retake policyNo mandatory waiting period. Retake fee applies.

Certification validity2 years

Recertification required every 2 years by taking the current exam version.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: DB-SPARK

01Stateful UDFs and StateStores
Treating a stateful UDF backed by a StateStore the same as a stateless one, without accounting for state persisting across micro-batches, leads to wrong answers on advanced-UDF questions.
02Save Modes on Files
Confusing overwrite, append, ignore, and error save modes when writing query results back to files leads to wrong answers on Spark SQL output questions.
03AQE Benefits
Not knowing what Adaptive Query Execution actually optimizes at runtime, such as skewed join handling and shuffle partition sizing, leads to wrong answers on performance-tuning questions.
04Streaming Dedup Watermarks
Deduplicating a streaming DataFrame without a watermark, so state grows unbounded, instead of bounding it correctly for late data, leads to wrong answers on structured-streaming questions.
05Pandas UDF vs API
Confusing a Pandas UDF, which vectorizes a custom function, with Pandas API on Spark itself, which reimplements pandas syntax on distributed DataFrames, leads to wrong answers on Pandas-on-Spark questions.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the Databricks Certified Associate Developer for Apache Spark exam?

The Databricks Certified Associate Developer for Apache Spark exam has 45 questions and a 90-minute time limit.

Which pitfalls should I review when preparing for Databricks Certified Associate Developer for Apache Spark?

Topics to review include Stateful UDFs and StateStores, Save Modes on Files, AQE Benefits, Streaming Dedup Watermarks, Pandas UDF vs API. Work through examples to check that you understand the distinctions and can explain your answer.

What do you need before the Databricks Spark developer exam?

There is no formal prerequisite. Databricks recommends at least six months of hands-on Spark development in Python or Scala, which matches how the questions are written: they assume you have hit these APIs rather than read about them.

How long is the Databricks Spark certification valid?

Two years, which is shorter than most cloud certifications. Recertification means taking the then-current version of the exam rather than a reduced renewal assessment, so plan for a full sitting.

Which topics carry the most weight on the Spark developer exam?

The DataFrame and DataSet API is the largest domain at 30%, with Spark architecture and Spark SQL at 20% each. Troubleshooting and tuning and structured streaming sit at 10% each, and Spark Connect and the pandas API on Spark at 5% each. Half the exam is therefore the DataFrame API plus architecture.

What happens if you fail the Spark developer exam?

Databricks sets no mandatory waiting period, so you can rebook as soon as you are ready. A retake fee applies for each attempt.

One certification, 12 months

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