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Amazon SAA-C03 Exam Questions
- Amazon Associate Certifications
- Amazon AWS Certified Solutions Architect Associate Certifications
- Topic 1: Design Secure Architectures: This section of the exam covers designing secure access to AWS resources.
- Topic 2: Design Resilient Architectures: This topic of the exam covers designing resilient architectures by ensuring scalability.
- Topic 3: Design High-Performing Architectures: This section of the exam covers determining high-performing and/or scalable storage solutions.
- Topic 4: Design Cost-Optimized Architectures: This section of the exam covers designing cost-optimized storage solutions.
Free Amazon SAA-C03 Exam Actual Questions
Note: Premium Questions for SAA-C03 were last updated On Apr. 10, 2025 (see below)
A solutions architect needs to optimize a large data analytics job that runs on an Amazon EMR cluster. The job takes 13 hours to finish. The cluster has multiple core nodes and worker nodes deployed on large, compute-optimized instances.
After reviewing EMR logs, the solutions architect discovers that several nodes are idle for more than 5 hours while the job is running. The solutions architect needs to optimize cluster performance.
Which solution will meet this requirement MOST cost-effectively?
EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload. This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.
Option A: Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.
Option C: Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.
Option D: Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.
AWS Documentation Reference:
A developer used the AWS SDK to create an application that aggregates and produces log records for 10 services. The application delivers data to an Amazon Kinesis Data Streams stream.
Each record contains a log message with a service name, creation timestamp, and other log information. The stream has 15 shards in provisioned capacity mode. The stream uses service name as the partition key.
The developer notices that when all the services are producing logs, ProvisionedThroughputExceededException errors occur during PutRecord requests. The stream metrics show that the write capacity the applications use is below the provisioned capacity.
How should the developer resolve this issue?
Partition Key Issue:
Using 'service name' as the partition key results in uneven data distribution. Some shards may become hot due to excessive logs from certain services, leading to throttling errors.
Changing the partition key to 'creation timestamp' ensures a more even distribution of records across shards.
Incorrect Options Analysis:
Option A: On-demand capacity mode eliminates throughput management but is more expensive and does not address the root cause.
Option B: Adding more shards does not solve the issue if the partition key still creates hot shards.
Option D: Using separate streams increases complexity and is unnecessary.
An e-commerce company has an application that uses Amazon DynamoDB tables configured with provisioned capacity. Order data is stored in a table named Orders. The Orders table has a primary key of order-ID and a sort key of product-ID. The company configured an AWS Lambda function to receive DynamoDB streams from the Orders table and update a table named Inventory. The company has noticed that during peak sales periods, updates to the Inventory table take longer than the company can tolerate. Which solutions will resolve the slow table updates? (Select TWO.)
:
Key Problem:
Delayed Inventory table updates during peak sales.
DynamoDB Streams and Lambda processing require optimization.
Analysis of Options:
Option A: Adding a GSI is unrelated to the issue. It does not address stream processing delays or capacity issues.
Option B: Optimizing batch size reduces latency and allows the Lambda function to process larger chunks of data at once, improving performance during peak load.
Option C: Increasing write capacity for the Inventory table ensures that it can handle the increased volume of updates during peak times.
Option D: Increasing read capacity for the Orders table does not directly resolve the issue since the problem is with updates to the Inventory table.
Option E: Increasing Lambda timeout only addresses longer processing times but does not solve the underlying throughput problem.
AWS Reference:
DynamoDB Streams Best Practices
Provisioned Throughput in DynamoDB
A developer is creating an ecommerce workflow in an AWS Step Functions state machine that includes an HTTP Task state. The task passes shipping information and order details to an endpoint.
The developer needs to test the workflow to confirm that the HTTP headers and body are correct and that the responses meet expectations.
Which solution will meet these requirements?
State Machine Testing with Logs:
Changing the log level to ALL enables capturing detailed request and response data. This helps verify HTTP headers, body, and responses.
Incorrect Options Analysis:
Option A and B: The TestState API is not a valid option for Step Functions.
Option C: A data flow simulator does not exist for AWS Step Functions.
A company is deploying a critical application by using Amazon RDS for MySQL. The application must be highly available and must recover automatically. The company needs to support interactive users (transactional queries) and batch reporting (analytical queries) with no more than a 4-hour lag. The analytical queries must not affect the performance of the transactional queries.
Key Requirements:
High availability and automatic recovery.
Separate transactional and analytical queries with minimal performance impact.
Allow up to a 4-hour lag for analytical queries.
Analysis of Options:
Option A:
Multi-AZ deployments provide high availability but do not include read replicas for separating transactional and analytical queries.
Analytical queries on the secondary DB instance would impact the transactional workload.
Incorrect Approach: Does not meet the requirement of query separation.
Option B:
Multi-AZ DB clusters provide high availability and include a reader endpoint. However, these are better suited for Aurora and not RDS for MySQL.
Incorrect Approach: Not applicable to standard RDS for MySQL.
Option C:
Multiple read replicas allow separation of transactional and analytical workloads.
Queries can be pointed to a replica in a different AZ, ensuring no impact on transactional queries.
Correct Approach: Meets all requirements with high availability and query separation.
Option D:
Creating nightly snapshots and read-only databases adds significant operational overhead and does not support the 4-hour lag requirement.
Incorrect Approach: Not practical for dynamic query separation.
AWS Solution Architect Reference:
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