CLF-C02 Cloud Technology and Services Practice Question
A data engineering team processes petabytes of raw log data using Apache Spark and Hadoop frameworks. They need a managed AWS service that provisions the cluster, installs the big data frameworks, and terminates the cluster after the job completes to minimise cost. Which service should they use?
⚠ Common exam trap
Candidates often confuse AWS Glue's use of Apache Spark with the ability to run custom Hadoop/Spark jobs on petabyte-scale data, but Glue is serverless and lacks the cluster management and framework installation capabilities required for this use case.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon EMR
Amazon EMR is the correct choice because it is a managed big data platform that can provision Apache Spark and Hadoop clusters, install the required frameworks, and automatically terminate the cluster upon job completion using features like transient clusters and step-based lifecycle management. This minimizes cost by only paying for compute resources during active processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is a fully managed petabyte-scale data warehouse that uses columnar storage and massively parallel processing (MPP) to execute SQL analytic queries. While Redshift Spectrum can scan data in S3, it still only supports SQL and does not run Hadoop, Spark, or MapReduce jobs. Redshift is optimized for business intelligence and reporting workloads, not as a general-purpose big data processing framework where developers need fine-grained control over cluster configuration or custom code.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a serverless ETL service that can run Apache Spark jobs using dynamically provisioned resources. However, Glue abstracts away cluster management and automatically scales compute, giving you limited control over Spark memory, executor settings, and framework add-ons. For large, complex Spark workloads that require custom cluster tuning, persistent cluster lifetimes, or integration with specialized ecosystem tools like Hive or HBase, Amazon EMR offers far more flexibility and configurability.
- ✓
Amazon EMR
Why this is correct
Amazon EMR is AWS's managed big data platform that automatically provisions and configures clusters running Hadoop, Spark, Hive, Presto, HBase, and dozens of other open-source frameworks. It supports transient clusters that launch, process data, and terminate automatically, reducing cost for batch workloads since you only pay for compute time during the job. EMR also integrates natively with S3 and can leverage spot instances, making it the intended service for users who need full control over their big data infrastructure.
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena is a serverless interactive query service that runs standard SQL directly against data stored in Amazon S3, using Presto/Trino under the hood. It does not provision servers or manage clusters and cannot execute Java-based Hadoop, Spark, Hive, or other big data frameworks. Athena is designed for ad-hoc analytics and geospatial queries, not for orchestrating multi-step batch processing pipelines or running custom MapReduce jobs.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CLF-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CLF-C02 exam.