Download Full Version SAP-C02 Amazon Web Services Exam

The compute layer uses AWS Fargate, so the correct savings instrument is a Compute Savings Plan, not an EC2 Instance Savings Plan. AWS states that Compute Savings Plans automatically apply to EC2, Fargate, and Lambda usage, while EC2 Instance Savings Plans are restricted to EC2 instance usage in a selected family and Region. The database layer is Aurora MySQL, so a Reserved DB Instance is appropriate only for the predictable production database, not for development and testing databases that are regularly shut down. A 1-year commitment also matches the stated one-year predictable production demand. Buying 3-year commitments for all accounts would overcommit spend, especially for non-production environments with intermittent usage. Therefore, centralized 1-year production database reservations plus a 1-year Compute Savings Plan is the most cost-effective match.

The company requires two different types of security analysis: vulnerability scanning for CVEs and code-level scanning for sensitive data exposure. Amazon Inspector provides both Lambda Standard Scanning and Lambda Code Scanning. These capabilities allow Inspector to examine Lambda deployment packages and Lambda layers for software vulnerabilities, and also perform static code analysis to detect insecure coding patterns. To meet the requirement that only certain Lambda functions receive deeper code scanning, Inspector allows enabling or disabling code scanning at the individual Lambda function level through the function ' s Monitor settings. Inspector additionally supports resource exclusion based on tagging, allowing administrators to prevent specific Lambda functions from participating in code scans. Tag-based exclusion satisfies the requirement to scan only a subset of Lambda functions.

Option B is required because enabling both Lambda standard scanning and Lambda code scanning activates the functionality necessary for CVE detection and code security analysis.

Option D is required because enabling scanning at the function level ensures that code scanning runs only for selected Lambda functions.

Option E is required because resource exclusion using tags allows the administrator to exclude non-target Lambda functions from code scanning automatically.

Options A and C do not satisfy the requirement because simply activating Amazon Inspector does not configure per-function scanning behavior, and GuardDuty Lambda Protection does not scan for CVEs or code vulnerabilities. Option F is not valid because Amazon Inspector does not perform scans directly against objects stored in Amazon S3; scanning occurs against the deployed Lambda function versions themselves.

The modernized application needs to store and serve video files up to 4 GB. Large binary content should not be stored directly in a database because it negatively affects database performance, scaling, and backup/restore times. The standard AWS pattern is to store large objects in Amazon S3 and keep only references (such as keys or URLs) in the database.

User profile data is simple, text-based, and currently lives in a single table. This is a good fit for a key-value or document-style data model such as Amazon DynamoDB, which provides fully managed horizontal scaling and predictable performance at scale.

Option B migrates the profile table to DynamoDB using AWS DMS with AWS Schema Conversion Tool (SCT). Video files are stored as objects in Amazon S3, which is designed for large, durable, and highly scalable object storage. The DynamoDB item stores only the S3 key that corresponds to each user’s video, keeping the database small and responsive. S3 can scale rapidly and independently of the database, and offloads the heavy I/O load of large objects away from the transactional store, so overall application performance is not negatively impacted by video storage or retrieval.

Option A stores the videos as base64-encoded strings in a TEXT column in a relational database. Storing multi-gigabyte objects directly in a relational database table leads to large row sizes, larger indexes, longer backup times, and degraded performance as the table grows. This does not meet the requirement to avoid affecting application performance.

Option C uses Amazon Keyspaces (for Apache Cassandra) as the profile store and S3 for video. While storing videos in S3 is correct, Keyspaces is typically chosen when there is already a Cassandra-based workload or a need for Cassandra-compatible interfaces. For a simple single-table user profile store being migrated from MySQL, DynamoDB is the more natural, managed choice and aligns better with AWS migration and modernization guidance.

Option D stores the videos as base64-encoded strings in DynamoDB items. DynamoDB has a maximum item size of 400 KB, which is far smaller than the 4 GB video size requirement. This makes option D technically infeasible.

Therefore, migrating user profile data to DynamoDB and storing video files in Amazon S3, with S3 keys stored in the corresponding DynamoDB items (option B), provides scalable video storage and protects application performance.