A team is considering Spark for a transactional system that requires strict consistency guarantees. Which limitation of Spark is most relevant to this requirement?

Spark cannot process data in parallel across multiple nodes.

Spark requires a dedicated cluster for every job, increasing complexity.

Spark does not support SQL queries, limiting its use in transactional systems.

Spark does not provide full ACID compliance for all data sources.

A company runs Spark jobs on a cluster with limited resources. They notice that some jobs fail with out-of-memory errors. They suspect that the default memory settings are not appropriate for their workload. Which challenge of Spark does this reflect?

Inability to handle large datasets

A logistics company has a team of data engineers who are proficient in Python and SQL. They want to build a data processing pipeline that includes batch ETL, machine learning, and graph analytics. They are evaluating Spark. Which advantage of Spark is most relevant to their team's skill set and diverse needs?

High-level APIs in multiple languages

Real-time data ingestion from all sources

DatabricksCertified Associate Developer for Apache Spark

Domain 1Objective 1

Identify the Advantages and Challenges of Implementing Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

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 6–10

6application · medium
An e-commerce company wants to scale their data processing as their user base grows. They need a solution that can handle increasing data volumes by adding more machines. Which advantage of Spark is most relevant?
Select an answer first
7expert · hard
A company is running a Spark streaming job that processes sensor data. They need to ensure that each event is processed exactly once, even if a node fails. They are aware that Spark's default processing semantics may not guarantee this. Which challenge of Spark does this scenario highlight?
Select an answer first
8foundation · easy
A team is considering Spark for a transactional system that requires strict consistency guarantees. Which limitation of Spark is most relevant to this requirement?
Select an answer first
9application · medium
A company runs Spark jobs on a cluster with limited resources. They notice that some jobs fail with out-of-memory errors. They suspect that the default memory settings are not appropriate for their workload. Which challenge of Spark does this reflect?
Select an answer first
10application · medium
A logistics company has a team of data engineers who are proficient in Python and SQL. They want to build a data processing pipeline that includes batch ETL, machine learning, and graph analytics. They are evaluating Spark. Which advantage of Spark is most relevant to their team's skill set and diverse needs?
Select an answer first

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Identify the Advantages and Challenges of Implementing Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

Questions 6–10

An e-commerce company wants to scale their data processing as their user base grows. They need a solution that can handle increasing data volumes by adding more machines. Which advantage of Spark is most relevant?

A company is running a Spark streaming job that processes sensor data. They need to ensure that each event is processed exactly once, even if a node fails. They are aware that Spark's default processing semantics may not guarantee this. Which challenge of Spark does this scenario highlight?

A team is considering Spark for a transactional system that requires strict consistency guarantees. Which limitation of Spark is most relevant to this requirement?

A company runs Spark jobs on a cluster with limited resources. They notice that some jobs fail with out-of-memory errors. They suspect that the default memory settings are not appropriate for their workload. Which challenge of Spark does this reflect?

Keep studying — Apache Spark Architecture and Components