Free Top Amazon/AWS DEA-C01 Actual Exam Questions

It’s B for sure. ACM handles certs automatically and works with Elastic Load Balancers in front of EC2, so you avoid manual cert management or complex scripting.

Makes sense to skip A since self-managing certs means more manual work and risk. B seems best for auto-renewal without extra scripting hassle. Going with B here.

Good point about RDS engine support being crucial. Still, option B makes the most sense since Glue crawlers can recognize formats automatically, reducing manual steps compared to A or D.

B Glue crawlers are designed to detect data formats automatically, so they handle this with minimal manual work. This beats A since it avoids relying on data stewards.

D, because OpenSearch is built for log and trace correlation with minimal extra setup.

Option A is solid because FluentBit and OpenTelemetry are designed to work seamlessly for logs and traces collection in Kubernetes environments, minimizing custom development. For correlating those logs and traces, option D makes the most sense—Amazon OpenSearch can index both data types and supports search queries that help identify failure points across the request flow. The other options either complicate trace collection or add unnecessary processing layers. Using OpenSearch directly for analysis also avoids spinning up extra ETL processes like with Glue, so A and D together meet the requi

It’s B because compliance mode blocks root user from deleting or modifying objects during retention.

B makes sense since compliance mode locks objects from root changes, but I'm unsure if default retention applies automatically. Does the default retention really cover all objects without setting it individually?

Maybe A here. The Spark UI is designed to show detailed metrics for each stage of a Glue job since Glue runs on Apache Spark. CloudWatch (B) usually shows overall job metrics but not the fine-grained stage details. CloudTrail (C) is more about API calls, so it wouldn’t have stage metrics. Run Insights (D) sounds useful but it depends on the Glue version, which isn’t specified, so it feels less certain. Given that, checking the Spark UI directly seems like the most reliable way to get stage-level info.

A Spark UI gives a direct look at each stage’s performance, so it’s handy if you want detailed metrics without extra features. It’s built for that kind of granular Spark info.

It’s B here because it uses CREATE TABLE AS with the right WHERE filter for US cities and explicitly defines the table location, which Athena needs to know to create the new table properly. Options C and D miss setting the location, so they might fail or default wrongly. Option A doesn’t filter at all. So B covers filtering and table creation details more completely.

C/D? Option C creates the new table with SELECT but doesn’t filter for US cities, while D filters correctly. So D seems more aligned with the requirement to include only US cities.

Maybe D is right since EventBridge directly listens to Step Functions events, making it cleaner than relying on S3 or CloudTrail, which aren’t designed for real-time pipeline alerts.

Maybe D is the best choice here because EventBridge can catch state machine execution updates instantly, which fits the real-time alert need. Options A and B depend on S3 events, which might add delays and extra steps, so they don’t feel as direct. CloudTrail in C is more about logging API calls rather than tracking actual step success or failure statuses, so it could miss the nuances inside the pipeline. EventBridge’s integration with SNS makes it straightforward to send alerts whenever a step fails or succeeds, covering the pipeline more comprehensively.

Not A, using AWS CLI would mean manual scripts and more ongoing work. Storage Lens (C) offers a ready dashboard with insights on incomplete uploads and old versions, cutting down on operational effort.

It’s C. Storage Lens gives a broad overview with trends and metrics, including incomplete multipart uploads and outdated versions, all in one dashboard. It requires minimal setup and no manual report handling. Inventory reports (B) are detailed but more manual to process regularly. For quick, ongoing insights with less operational fuss, Storage Lens fits better since it’s designed for cost optimization visibility across buckets without extra scripting or data crunching.

C fits best here since tables are small and a full refresh nightly is simplest.

Maybe C works best here since the tables are small and refreshed nightly. Just overwriting the entire dataset with DMS full load avoids the complexity of incremental logic and fits the BI team's access timing well.

It’s not C because just upgrading instance size without increasing storage won’t fix root disk space issues. Also, restarting the cluster could cause downtime which isn’t needed here. Expanding storage is more direct.

D imo, specifying a target volume for a topic doesn’t affect the broker’s root disk usage directly. The issue is more about overall storage capacity on the brokers, so expanding storage like in A seems more relevant here.

It’s B because streaming data through Kinesis with Apache Flink lets you detect drops instantly without waiting for batch queries. That’s way faster than polling every minute like A or C.

C imo, querying DynamoDB with DAX should be faster than Aurora queries every minute.

Maybe D is the simplest if the SaaS apps already support exports, but it’s manual and won’t scale well for frequent updates or multiple apps. Not very efficient long term.

It’s C. AppFlow automates data extraction and transfer to S3 without custom coding, so it’s way less hands-on than building Lambda functions or managing Athena connectors. Much better for operational efficiency.

Maybe A is better because Step Functions natively orchestrate both Lambda and Glue jobs without extra infrastructure, unlike Airflow options which add more management overhead. Glue workflows likely don’t call Lambda directly yet.

D imo, running Airflow on EKS is definitely more complex and requires managing the cluster, so it adds way more overhead than a native AWS service like Step Functions.

What about B? Athena scales automatically and fits unpredictable spikes with no infra management.

A/D? Aurora is more OLTP, not analytics-optimized, so A fits better here.

I think B is a solid pick for real-time recommendations since it directly invokes the SageMaker endpoint. For the future inventory predictions, A seems like the best choice because Redshift ML can build models right inside Redshift, so it handles ongoing predictions without needing to export data elsewhere. C is more about offline training, which isn’t as useful for real-time or seamless future predictions. So I’d go with A and B here.

D imo because SageMaker Autopilot is focused on building models, not dashboards, so that’s out. E is definitely off since Redshift isn’t meant for file storage or archiving reports. That leaves A and B as the only viable options—A for using Redshift ML to handle predictions within the cluster, and B for invoking the SageMaker real-time endpoint directly. Makes sense to combine them to cover both real-time and future inventory needs without unnecessary steps.