Cloud Engineer Case Studies & Practical Application 2

Question 1

A fintech startup experiences intermittent latency spikes in their payment processing API hosted on AWS. CloudWatch shows CPU and memory are normal, but RDS connection errors appear in logs. What is the most likely cause?

Accepted Answer

RDS connection pool exhaustion under peak load

Answer

Connection pool exhaustion occurs when concurrent requests exceed the max_connections limit on RDS, causing new connections to fail even though compute resources appear healthy.

Question 2

A retail company migrates an on-premises Oracle database to Google Cloud. After migration, batch ETL jobs run 3x slower than before. The Cloud SQL instance matches the on-premises CPU/RAM specs. What should the engineer investigate first?

Accepted Answer

Increase Cloud SQL storage IOPS and check disk throughput limits

Answer

Cloud SQL storage IOPS are provisioned based on disk size; if the migrated database uses fast SAN storage on-premises, the default Cloud SQL disk may be the bottleneck.

Question 3

A media company uses Azure Blob Storage for video assets. Egress costs are unexpectedly high despite 80% of traffic coming from users in the same Azure region. What configuration is likely missing?

Accepted Answer

Azure CDN is not configured to serve assets from a nearby edge node

Answer

Without Azure CDN, all requests hit origin Blob Storage and incur egress charges; CDN caches assets at edge nodes, eliminating repeated origin egress for popular content.

Question 4

An e-commerce platform's checkout service on Kubernetes crashes every night at 2 AM. Pod logs show OOMKilled. The team set a 512Mi memory limit. Nightly batch jobs run at 1:45 AM. What is the best fix?

Accepted Answer

Increase the pod memory limit and add a Vertical Pod Autoscaler

Answer

The batch jobs likely cause memory spikes beyond 512Mi; raising the limit and using VPA ensures the pod has headroom while right-sizing automatically over time.

Question 5

A SaaS company stores tenant data in separate GCP projects for isolation. Cross-project BigQuery queries take 40 seconds. The same data in a single project queries in 2 seconds. What should the engineer recommend?

Accepted Answer

Use BigQuery Authorized Views to expose tenant datasets within a shared project

Answer

Authorized Views allow cross-project data access without data movement while maintaining IAM-level isolation, avoiding the network overhead of cross-project queries.

Cloud Engineer Practice Test

Cloud Engineer Practice Test

Cloud Engineer Case Studies & Practical Application 2