A data engineering team is evaluating Apache Spark for a new data processing platform. Which statement accurately describes a key advantage of using Spark?

Spark provides a single engine for batch processing, interactive queries, streaming, and machine learning.

Spark guarantees exactly-once processing semantics for all streaming workloads by default.

Spark is primarily designed for single-node processing to simplify deployment.

Spark automatically optimizes all queries without requiring any manual tuning or configuration.

A team is running a Spark job that performs a large shuffle operation. They notice that the job is slow and consumes excessive memory. They have tried increasing the number of executors, but performance did not improve. What is the most likely cause of this issue?

The job is not using the right language API

The cluster does not have enough nodes

Shuffle partitions are not optimally configured

The data is not cached in memory

A company wants to use Spark for both batch and streaming workloads. They have a limited budget and want to avoid maintaining separate clusters. They are considering using a single Spark cluster for both. Which advantage of Spark supports this approach?

Native support for all data sources

A financial services firm wants to use Spark for real-time fraud detection on streaming transactions. They also need to maintain a historical record of all transactions with strong consistency guarantees. Which challenge of Spark should they plan for?

Requires a dedicated cluster for each workload

Inability to process streaming data

Lack of full ACID compliance in standard Spark

DatabricksCertified Associate Developer for Apache Spark

Domain 1Objective 1

Identify the Advantages and Challenges of Implementing Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Part of the Apache Spark Architecture and Components 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 1–2 from this objective — we provide 20 practice questions to prepare you well beyond it. (estimate)

20questions here

4free pages

2concepts

20%of the exam

Questions 1–5

1application · medium
A data team is new to Spark and is struggling to optimize their jobs. They notice that some jobs are slow despite adding more executors. They are unsure how to configure memory, partitions, and shuffle settings. Which challenge of Spark does this scenario illustrate?
Select an answer first
2foundation · easy
A data engineering team is evaluating Apache Spark for a new data processing platform. Which statement accurately describes a key advantage of using Spark?
Select an answer first
3expert · hard
A team is running a Spark job that performs a large shuffle operation. They notice that the job is slow and consumes excessive memory. They have tried increasing the number of executors, but performance did not improve. What is the most likely cause of this issue?
Select an answer first
4expert · hard
A company wants to use Spark for both batch and streaming workloads. They have a limited budget and want to avoid maintaining separate clusters. They are considering using a single Spark cluster for both. Which advantage of Spark supports this approach?
Select an answer first
5application · medium
A financial services firm wants to use Spark for real-time fraud detection on streaming transactions. They also need to maintain a historical record of all transactions with strong consistency guarantees. Which challenge of Spark should they plan for?
Select an answer first

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