Amazon Web Services Updated DVA-C02 Exam Questions and Answers by artemis

The correct policy condition ensures that:

The LeadingKeys condition restricts operations to the authenticated user ' s user_id.

The Attributes condition limits the updatable attributes to user_name.

Explanation of Choices:

Option A: Correctly enforces both the key restriction (dynamodb:LeadingKeys) and ensures only the user_name attribute can be updated.

Option B, C, D: Use incorrect conditions, such as referencing user_name in the LeadingKeys or including other attributes like user_id in updatable fields.

The goal is to identify performance bottlenecks across a Lambda function that calls a database (DocumentDB). The most operationally efficient way to pinpoint where time is being spent—without building custom logging/metrics pipelines—is to use distributed tracing . AWS X-Ray provides end-to-end request traces, timing breakdowns, and service maps that help identify whether latency is dominated by Lambda initialization, downstream calls, or database queries.

By enabling X-Ray tracing on the Lambda function, the developer can capture traces for report-generation requests and examine segments that show duration spent inside the function and in downstream dependencies. To specifically diagnose database time, the developer can add custom subsegments around DocumentDB operations (query execution, connection acquisition, cursor iteration). This produces precise timing data for each step, making it straightforward to identify slow queries, excessive connection setup time, or serialization overhead. Because X-Ray aggregates and visualizes traces, the developer can quickly compare “fast” and “slow” traces and isolate the bottleneck.

Option B focuses on errors and generic metrics dashboards; it’s useful for monitoring, but it won’t precisely attribute the extra 22+ seconds to a particular downstream call path. Option C proposes performance improvements (pooling/async), but the question asks to identify bottlenecks first, and it also requires code changes and validation without confirming the root cause. Option D requires adding detailed logging statements and then querying logs; this is more development effort and more ongoing log volume/cost than turning on X-Ray and using trace analysis, especially when the intent is bottleneck identification rather than permanent instrumentation.

Therefore, A is the best choice: enable AWS X-Ray for the Lambda function and trace DocumentDB interactions with custom subsegments to quickly and efficiently identify the specific source(s) of increased latency.

The requirement is to analyze existing Lambda logs using a request ID that appears in both functions, and to do so with the least development effort . Amazon CloudWatch Logs Insights is purpose-built for interactive log analysis: it lets you run ad-hoc queries over CloudWatch log groups using a query language that supports filtering, parsing, sorting, and aggregation. This means you can quickly search across the relevant Lambda log groups and filter results to only entries that contain a specific request ID.

With Logs Insights, the developer can select both Lambda log groups, set a time range around the failure, and run a query such as filtering on the request ID field or matching the request ID string in the message. This provides a fast way to reconstruct the end-to-end flow and identify where the failure occurred—without writing any custom code, building data pipelines, or exporting logs.

Option A (using an AWS SDK) requires writing and maintaining code to call CloudWatch Logs APIs, handle pagination, time windows, filtering, and correlation across groups—more effort than necessary. Option B (export to S3 + Athena) adds significant setup (export tasks, S3 storage, Athena tables/partitions) and is better suited to large-scale analytics or long-term archival, not quick troubleshooting. Option D (Live Tail) is primarily for real-time streaming of new log events; it is less suitable for retrospective investigation of an incident that already happened, and it doesn’t provide the same structured querying and cross-log-group correlation capabilities as Logs Insights.

Therefore, CloudWatch Logs Insights (C) is the most direct, AWS-native, low-effort solution to query both Lambda log groups and filter by request IDs to troubleshoot the failing process flow.