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Databricks · DB-SPARK · Associate

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

The Databricks Certified Associate Developer for Apache Spark exam validates the ability to build applications using Apache Spark.

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90minPass-IT mock exam time limit

Checked against Databricks · September 2026 · Current exam version

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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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Conversation preview · DB-SPARK

Collecting to pandas

  1. You

    Why can to_pandas() crash with a large DataFrame?

  2. Alex · AI tutor

    It collects the distributed data onto the driver to create a regular pandas DataFrame.

    Read the Databricks documentation ↗
  3. You

    Does the result stay distributed?

  4. Alex · AI tutor

    No. The driver needs enough memory for the collected result.

    Read the Databricks documentation ↗

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DB-SPARK · Question excerpt

Collecting to pandas

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

Understand the correct answer.

All distributed data is collected from Spark executors to the driver and converted to a regular pandas DataFrame; this can cause OOM for large DataFrames

to_pandas() collects data to the driver, which can exhaust memory for large results.

Read the Databricks documentation ↗

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Exam domains

What you practice for DB-SPARK.

Exam topics, relevant practice and sources you can check.

Develop DataFrame applicationsSee the guide for weighting86 QuestionsPass-IT questions

Use Spark operations to reshape, validate and combine data.

  • Add, drop, split or rename columns, filter rows and explode arrays; deduplicate, validate and aggregate DataFrames.
  • Work with dates, convert Unix timestamps and extract date components.
  • Read, write or overwrite DataFrames with schemas, sort and iterate over them, inspect schemas and convert between DataFrames and lists.
  • Combine DataFrames with joins or unions, choose keys and explain when and how to use broadcast joins.
  • Create and invoke UDFs with or without stateful operators and StateStores, and explain broadcast variables and accumulators.
Official Databricks objectives
Understand Spark executionSee the guide for weighting34 QuestionsPass-IT questions

Connect distributed architecture to the way Spark runs work.

  • Explain Spark’s benefits and challenges and the roles of clusters, drivers, workers, executors, cores and memory.
  • Describe DataFrames, Datasets and the SparkSession lifecycle, including caching, storage levels and garbage collection.
  • Explain the execution hierarchy, actions, transformations and lazy evaluation, and configure partitions and shuffles for distributed processing.
  • Distinguish Spark Core, SQL, DataFrames, pandas API on Spark, Structured Streaming and machine learning modules.
Official Databricks objectives
Read and write with Spark SQLSee the guide for weighting65 QuestionsPass-IT questions

Use SQL to access files, DataFrames and persistent tables.

  • Read and write JDBC or file sources with suitable save modes, overwriting and column partitioning; query ORC, JSON, CSV, text and Delta files directly.
  • Save data as persistent tables and apply sorting and partitioning for retrieval.
  • Register DataFrames as temporary views and query them with SQL.
Official Databricks objectives
Tune and troubleshoot SparkSee the guide for weighting45 QuestionsPass-IT questions

Use partitioning and logs to improve resource use.

  • Tune partitions, repartitioning and coalescing, identify skew and reduce shuffling to improve cluster utilization.
  • Explain Adaptive Query Execution and its benefits.
  • Publish, customize and analyze driver and executor logs to diagnose memory errors and underused clusters.
Official Databricks objectives
Process streaming dataSee the guide for weighting40 QuestionsPass-IT questions

Apply DataFrame operations to data that keeps arriving.

  • Explain Structured Streaming’s programming model, micro-batches, exactly-once semantics and fault tolerance.
  • Create and write streaming DataFrames or Datasets with suitable output modes and sinks.
  • Apply selection, projection, windows and aggregation to streaming data.
  • Deduplicate streaming records with or without watermarks.
Official Databricks objectives
Understand application deploymentSee the guide for weighting13 QuestionsPass-IT questions

Recognize Spark Connect and the available execution modes.

  • Describe Spark Connect’s features.
  • Distinguish client, cluster and local deployment modes in Spark.
Official Databricks objectives
Use pandas with SparkSee the guide for weighting28 QuestionsPass-IT questions

Apply pandas-oriented tools in the Spark environment.

  • Explain the advantages of pandas API on Spark.
  • Create and invoke pandas UDFs.
Official Databricks objectives
Question formats you can practice
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Frequently asked questions

Your preparation

How much study time should I plan for DB-SPARK?

For planning, Pass-IT estimates roughly 50–120 hours. Relevant experience may put you toward the lower end; starting from scratch may put you toward the higher end. Your needs may fall outside this range. This is not a time requirement from Databricks or a promise that you will be exam-ready within that time.

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.

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What language are Pass-IT questions in?

Questions in the app are currently in English. Translations of the sample questions on this page apply only to this preview. Check with Databricks for the languages available for the official exam.

How long does my DB-SPARK access last?

Access covers this certification for 12 months. You pay once, with no automatic renewal.

When does the money-back guarantee apply?

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days. Guarantee terms

About the DB-SPARK exam

Where can I take the DB-SPARK exam?

Online proctored or test center.

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.

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.

What happens if you fail the Spark developer exam?

You must wait 14 days before retaking the Spark developer exam and between every further attempt. Databricks sets no limit on the number of retakes.

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Guarantee terms

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days.

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