Exam Associate-Developer-Apache-Spark-3.5 Topic 1 Question 25 Discussion
Actual exam question for Databricks's Associate-Developer-Apache-Spark-3.5 exam
Question #: 25
Topic #: 1
Question #: 25
Topic #: 1
23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)
Suggested Answer: A,E Vote an answer
Apache Spark is a distributed data processing engine designed for large-scale, cluster-based computation.
Advantages:
Horizontal Scalability: Spark can distribute tasks across many machines, handling datasets larger than the memory of a single node.
Fault Tolerance: Spark automatically recovers from node or task failures using the lineage graph (RDD recovery mechanism) and retry logic.
These two features allow Spark to process huge datasets efficiently and reliably, unlike standard Python scripts that are limited to one machine and fail on single-node errors.
Why the other options are incorrect:
B: Spark runs on commodity hardware; no specialized machines required.
C: Spark emphasizes in-memory processing, not disk-only operations.
D: Spark still requires user code in Python, Scala, SQL, or Java.
Reference:
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - advantages, cluster execution, and fault tolerance.
Apache Spark Overview - distributed processing and resilience design.
Advantages:
Horizontal Scalability: Spark can distribute tasks across many machines, handling datasets larger than the memory of a single node.
Fault Tolerance: Spark automatically recovers from node or task failures using the lineage graph (RDD recovery mechanism) and retry logic.
These two features allow Spark to process huge datasets efficiently and reliably, unlike standard Python scripts that are limited to one machine and fail on single-node errors.
Why the other options are incorrect:
B: Spark runs on commodity hardware; no specialized machines required.
C: Spark emphasizes in-memory processing, not disk-only operations.
D: Spark still requires user code in Python, Scala, SQL, or Java.
Reference:
Databricks Exam Guide (June 2025): Section "Apache Spark Architecture and Components" - advantages, cluster execution, and fault tolerance.
Apache Spark Overview - distributed processing and resilience design.
by Evelyn at Oct 05, 2026, 08:51 AM
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