CLF-C02 Cloud Technology and Services Practice Question
Which AWS service provides a serverless, fully managed Apache Spark processing engine for big data analytics without managing clusters?
⚠ Common exam trap
A common mix-up: candidates confuse Amazon EMR (which can run Spark but requires cluster management) with a fully serverless Spark offering, or they mistakenly think Amazon Kinesis Data Analytics supports Apache Spark when it actually supports Apache Flink and SQL for stream processing.
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
✓
AWS Glue (serverless Apache Spark ETL)
AWS Glue provides a fully managed, serverless Apache Spark environment for ETL (extract, transform, load) workloads. It automatically provisions, configures, and scales the Spark cluster behind the scenes, so you don't need to manage any infrastructure. This makes it the correct choice for a serverless Apache Spark processing engine for big data analytics.
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 EMR on EC2
Why it's wrong here
Amazon EMR on EC2 is a managed cluster platform, but it fundamentally requires you to provision, size, and manage EC2 instances for the cluster—even with EMR's management layer, you're responsible for choosing instance types, scaling policies, and patch/maintenance decisions. It is not serverless: you pay for the underlying EC2 instances regardless of whether your Spark jobs are actively using full capacity. Therefore, while EMR can run Apache Spark, it fails the serverless, zero-provisioning requirement central to this scenario.
- ✗
Amazon Redshift
Why it's wrong here
Amazon Redshift is a fully managed petabyte-scale data warehouse built for SQL-based analytics over columnar storage, not a general-purpose compute engine for Apache Spark. You cannot submit arbitrary Spark ETL jobs to Redshift; it uses its own query execution engine and SQL interface, with Redshift Spectrum for querying data in S3 but still through SQL. Since the workload explicitly requires running Apache Spark ETL code without managing infrastructure, Redshift does not match the technical or architectural requirement.
- ✓
AWS Glue (serverless Apache Spark ETL)
Why this is correct
AWS Glue provides a serverless Apache Spark environment purpose-built for ETL, meaning you do not provision, configure, or scale any cluster—AWS handles the underlying resources automatically. Glue scales compute based on the job's DPU (data processing unit) requirements and charges per DPU-second only while your job runs, which directly matches the need for a serverless Spark ETL service. This makes it the correct choice for running Spark ETL workloads without operational overhead.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Amazon Kinesis Data Analytics is designed for continuous stream processing using SQL or Apache Flink, not for batch-oriented Apache Spark ETL jobs. It consumes and processes data in near-real time from sources like Kinesis Data Streams or Kafka, whereas the question involves a typical batch ETL workload (e.g., reading from S3, transforming, writing back). Even though it is serverless, it does not support Apache Spark as a runtime, so it is fundamentally the wrong tool for this Spark ETL use case.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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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.