SAA-C03 Design High-Performing Architectures Practice Question
A financial analytics team runs a batch job every night that scans a 4 TB Amazon Redshift provisioned cluster table to compute aggregates for a reporting dashboard. The dashboard queries are read-only, run for several hours each morning, and compete with ETL writes on the same cluster, causing slow dashboard response times. The team wants to isolate the dashboard workload and improve query performance without changing the ETL job. Which solution meets these requirements with the LEAST operational effort?
⚠ Common exam trap
The trap here is reaching for concurrency scaling as the isolation mechanism, when it only adds burst capacity to the same cluster and does not separate the dashboard from the ETL workload's data and leader-node resources.
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
✓
Create a Redshift Serverless workgroup and use Redshift data sharing to expose the provisioned cluster's data to the serverless workgroup for the dashboard queries.
Redshift data sharing separates compute so the dashboard reads live producer data through a serverless consumer without copying or snapshotting. That removes contention with the ETL writes and lets the dashboard scale independently, all with minimal setup. Concurrency scaling still shares the cluster, snapshot restores give stale data, and moving to Athena requires a costly migration and SQL rewrite.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable concurrency scaling on the provisioned cluster and configure a workload management (WLM) queue for the dashboard queries.
Why it's wrong here
Concurrency scaling adds transient clusters for bursts of concurrent queries, but it still shares the same data and can be constrained by the main cluster's capacity and WLM configuration. It does not fully isolate the dashboard from the nightly ETL's resource consumption on the leader and compute nodes. The team would still need to tune WLM queues, which is more operational effort than data sharing.
- ✗
Take a nightly snapshot of the cluster and restore it into a second provisioned cluster that serves only the dashboard queries.
Why it's wrong here
A restored cluster does isolate the dashboard, but the data is only as fresh as the last snapshot, so morning queries would not reflect the completed ETL run. Restoring a 4 TB cluster nightly also takes significant time and incurs duplicate storage and compute costs. Maintaining two clusters and refresh logic adds substantial operational overhead compared with data sharing.
- ✗
Move the table to Amazon S3 and query it with Amazon Athena, pointing the dashboard at the Athena results.
Why it's wrong here
Athena is serverless and would remove contention from Redshift, but migrating a 4 TB table, converting it to a columnar format, and rewriting the dashboard's SQL is a major project. Athena performance depends on file layout and partitioning, and it cannot natively use Redshift-specific features the analytics team may rely on. This is far more operational effort than isolating reads with data sharing.
- ✓
Create a Redshift Serverless workgroup and use Redshift data sharing to expose the provisioned cluster's data to the serverless workgroup for the dashboard queries.
Why this is correct
Redshift data sharing lets a Redshift Serverless workgroup read live data from a provisioned cluster without copying it, isolating the dashboard's compute from the ETL writes. The dashboard gets its own automatically scaled compute, so morning queries no longer contend with the batch job, and no data movement or application rewrite is required. This is the lowest-effort way to separate read and write workloads.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This SAA-C03 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 SAA-C03 exam.