A retail company processes clickstream data from millions of users daily. They currently use a batch ETL pipeline with Hadoop MapReduce, which takes several hours. They want to reduce processing time and also run interactive SQL queries on the same data. They are considering Apache Spark. Which advantage of Spark best addresses both requirements?

In-memory processing and unified analytics engine

Native support for MapReduce API compatibility

Automatic optimization without any configuration

Full ACID compliance for transactional integrity

A startup is deploying Spark on a small cluster with limited memory. They plan to process large datasets that do not fit in memory. They are concerned about performance degradation. Which challenge of Spark should they address?

No support for batch processing

Inability to handle large datasets

A marketing analytics team wants to run both batch ETL jobs and ad-hoc SQL queries on the same dataset without maintaining separate pipelines. They are evaluating Spark. Which advantage of Spark directly supports this use case?

Unified engine for batch and SQL

DatabricksCertified Associate Developer for Apache Spark

Domain 1Objective 1

Identify the Advantages and Challenges of Implementing Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 3)

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 11–15

11application · medium
A retail company processes clickstream data from millions of users daily. They currently use a batch ETL pipeline with Hadoop MapReduce, which takes several hours. They want to reduce processing time and also run interactive SQL queries on the same data. They are considering Apache Spark. Which advantage of Spark best addresses both requirements?
Select an answer first
12application · medium
A startup is deploying Spark on a small cluster with limited memory. They plan to process large datasets that do not fit in memory. They are concerned about performance degradation. Which challenge of Spark should they address?
Select an answer first
13expert · hard
A company is evaluating Spark for a new data platform. They have a team of data scientists who use Python and a team of data engineers who use Scala. They want to share code and collaborate on the same data processing pipelines. Which advantage of Spark is most important for this collaboration?
Select an answer first
14application · medium
A marketing analytics team wants to run both batch ETL jobs and ad-hoc SQL queries on the same dataset without maintaining separate pipelines. They are evaluating Spark. Which advantage of Spark directly supports this use case?
Select an answer first
15expert · hard
A company is considering Spark for a new data platform. They have a team of data engineers who are experienced with SQL and want to minimize the learning curve. They also need to support complex data transformations. Which advantage of Spark would be most beneficial for this team?
Select an answer first

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Identify the Advantages and Challenges of Implementing Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 3)

Questions 11–15

A startup is deploying Spark on a small cluster with limited memory. They plan to process large datasets that do not fit in memory. They are concerned about performance degradation. Which challenge of Spark should they address?

A company is evaluating Spark for a new data platform. They have a team of data scientists who use Python and a team of data engineers who use Scala. They want to share code and collaborate on the same data processing pipelines. Which advantage of Spark is most important for this collaboration?

A marketing analytics team wants to run both batch ETL jobs and ad-hoc SQL queries on the same dataset without maintaining separate pipelines. They are evaluating Spark. Which advantage of Spark directly supports this use case?

A company is considering Spark for a new data platform. They have a team of data engineers who are experienced with SQL and want to minimize the learning curve. They also need to support complex data transformations. Which advantage of Spark would be most beneficial for this team?

Keep studying — Apache Spark Architecture and Components