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| Section | Weight | Objectives |
|---|---|---|
| Deployment | 24% | - Infrastructure as code
|
| Development with AWS Services | 32% | - AWS core services development
|
| Troubleshooting and Optimization | 18% | - Performance optimization
|
| Security | 26% | - Application authentication and authorization
|
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NEW QUESTION # 343
A food-delivery company has an application that uses an Amazon DynamoDB table for customer orders. The table is in on-demand mode. The table uses order_date as the partition key. The table uses a combination of customer_id and order_id as the sort key (customer_id#order_id). During peak order periods, write operations to the table are being throttled. What should a developer do to resolve this issue?
Answer: C
Explanation:
The correct answer is D because the current DynamoDB table design is causing a hot partition problem. The table uses order_date as the partition key, which means that during peak ordering periods, a very large number of writes are likely targeting the same partition key value for the current date. In DynamoDB, write throttling often occurs when traffic is not evenly distributed across partition keys, even when the table uses on- demand mode . On-demand capacity can automatically scale, but it still depends on proper key distribution to avoid concentrated activity on a small number of partitions.
By changing the partition key to customer_id and using order_id as the sort key, writes will be spread across many customers instead of being concentrated on a small number of date values. This creates a more balanced write pattern and better aligns with DynamoDB best practices for high-scale workloads. DynamoDB documentation emphasizes choosing partition keys with high cardinality and even request distribution to avoid throttling and improve performance.
Option A is not a good design because a sequential partition key can still create uneven access patterns and usually does not support meaningful access requirements. Option B is not the best answer because simply increasing capacity does not fix a poor partition key design; hot partitions can still throttle. Option C is unnecessary because the issue is not that DynamoDB is the wrong service, but that the table schema needs redesign.
Therefore, the best solution is to refactor the key schema so that writes distribute more evenly across partitions, which is exactly what D achieves.
NEW QUESTION # 344
A developer has built an application that inserts data into an Amazon DynamoDB table. The table is configured to use provisioned capacity. The application is deployed on a burstable nano Amazon EC2 instance. The application logs show that the application has been failing because of a ProvisionedThroughputExceededException error.
Which actions should the developer take to resolve this issue? (Choose two.)
Answer: B,D
NEW QUESTION # 345
A developer is troubleshooting an application mat uses Amazon DynamoDB in the uswest-2 Region. The application is deployed to an Amazon EC2 instance. The application requires read-only permissions to a table that is named Cars The EC2 instance has an attached IAM role that contains the following IAM policy.
When the application tries to read from the Cars table, an Access Denied error occurs.
How can the developer resolve this error?
Answer: B
Explanation:
Explanation
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/access-control-overview.html#access-con
NEW QUESTION # 346
A company stores its data in data tables in a series of Amazon S3 buckets. The company received an alert that customer credit card information might have been exposed in a data table on one of the company's public applications. A developer needs to identify all potential exposures within the application environment.
Which solution will meet these requirements?
Answer: A
Explanation:
Requirement Summary:
Customer credit card data may be exposed
Data is stored in Amazon S3
Developer must identify all exposure risks
Tool to Use:
Amazon Macie is designed to:
Automatically scan S3 for sensitive data
Detect financial information, PII, credentials, etc.
Finding Type Mapping:
Credit card data maps to: SensitiveData:S3Object/Financial
Evaluate Options:
A). Athena + filtering
Athena is a query engine; it doesn't detect sensitive data automatically B). Macie + Financial finding type Correct Designed for this use case C). Macie + Personal finding type Personal maps to names, addresses, etc., not credit cards D). Athena + Financial Again, Athena can't classify data - it only queries structured data Macie Overview: https://docs.aws.amazon.com/macie/latest/userguide/what-is-macie.html Finding Types: https://docs.aws.amazon.com/macie/latest/user/findings-types.html Financial finding type: SensitiveData:S3Object/Financial
NEW QUESTION # 347
A developer has an application that makes batch requests directly to Amazon DynamoDB by using the BatchGetItem low-level API operation. The responses frequently return values in the UnprocessedKeys element.
Which actions should the developer take to increase the resiliency of the application when the batch response includes values in UnprocessedKeys? (Choose two.)
Answer: A,D
Explanation:
Explanation
The UnprocessedKeys element indicates that the BatchGetItem operation did not process all of the requested items in the current response. This can happen if the response size limit is exceeded or if the table's provisioned throughput is exceeded. To handle this situation, the developer should retry the batch operation with exponential backoff and randomized delay to avoid throttling errors and reduce the load on the table.
The developer should also use an AWS SDK to make the requests, as the SDKs automatically retry requests that return UnprocessedKeys.
References:
[BatchGetItem - Amazon DynamoDB]
[Working with Queries and Scans - Amazon DynamoDB]
[Best Practices for Handling DynamoDB Throttling Errors]
NEW QUESTION # 348
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