Amazon SAA-C03 Online Practice Questions and Exam Preparation

Amazon SAA-C03 Online Questions & Answers

• Question 1181:

  A company is building an application on AWS that connects to an Amazon RDS database. The company wants to manage the application configuration and to securely store and retrieve credentials for the database and other services.

  Which solution will meet these requirements with the LEAST administrative overhead?

  A. Use AWS AppConfig to store and manage the application configuration. Use AWS Secrets Manager to store and retrieve the credentials.
  B. Use AWS Lambda to store and manage the application configuration. Use AWS Systems Manager Parameter Store to store and retrieve the credentials.
  C. Use an encrypted application configuration file. Store the file in Amazon S3 for the application configuration. Create another S3 file to store and retrieve the credentials.
  D. Use AWS AppConfig to store and manage the application configuration. Use Amazon RDS to store and retrieve the credentials.
  A. Use AWS AppConfig to store and manage the application configuration. Use AWS Secrets Manager to store and retrieve the credentials.

• Question 1182:

  A solutions architect is designing a payment processing application that runs on AWS Lambda in private subnets across multiple Availability Zones. The application uses multiple Lambda functions and processes millions of transactions each day. The architecture must ensure that the application does not process duplicate payments.

  Which solution will meet these requirements?

  A. Use Lambda to retrieve all due payments. Publish the due payments to an Amazon S3 bucket. Configure the S3 bucket with an event notification to invoke another Lambda function to process the due payments.
  B. Use Lambda to retrieve all due payments. Publish the due payments to an Amazon Simple Queue Service (Amazon SQS) queue. Configure another Lambda function to poll the SQS queue and to process the due payments.
  C. Use Lambda to retrieve all due payments. Publish the due payments to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Configure another Lambda function to poll the FIFO queue and to process the due payments.
  D. Use Lambda to retrieve all due payments. Store the due payments in an Amazon DynamoDB table. Configure streams on the DynamoDB table to invoke another Lambda function to process the due payments.
  C. Use Lambda to retrieve all due payments. Publish the due payments to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Configure another Lambda function to poll the FIFO queue and to process the due payments.

• Question 1183:

  A finance company uses backup software to back up its data to physical tape storage on-premises. To comply with regulations, the company needs to store the data for 7 years. The company must be able to restore archived data within one week when necessary.

  The company wants to migrate the backup data to AWS to reduce costs. The company does not want to change the current backup software.

  Which solution will meet these requirements MOST cost-effectively?

  A. Use AWS Storage Gateway Tape Gateway to copy the data to virtual tapes. Use AWS DataSync to migrate the virtual tapes to the Amazon S3 Standard-Infrequent Access (S3 Standard-IA). Change the target of the backup software to S3 Standard-IA.
  B. Convert the physical tapes to virtual tapes. Use AWS DataSync to migrate the virtual tapes to Amazon S3 Glacier Flexible Retrieval. Change the target of the backup software to the S3 Glacier Flexible Retrieval.
  C. Use AWS Storage Gateway Tape Gateway to copy the data to virtual tapes. Migrate the virtual tapes to Amazon S3 Glacier Deep Archive. Change the target of the backup software to the virtual tapes.
  D. Convert the physical tapes to virtual tapes. Use AWS Snowball Edge storage-optimized devices to migrate the virtual tapes to Amazon S3 Glacier Flexible Retrieval. Change the target of the backup software to S3 Glacier Flexible Retrieval.
  C. Use AWS Storage Gateway Tape Gateway to copy the data to virtual tapes. Migrate the virtual tapes to Amazon S3 Glacier Deep Archive. Change the target of the backup software to the virtual tapes.

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  Explanation

  AWS Storage Gateway Tape Gateway provides a seamless way to migrate backup data to AWS without requiring changes to the backup software. Migrating to S3 Glacier Deep Archive ensures long-term, cost-effective storage for data that rarely needs retrieval.

  Option A:S3 Standard-IA is more expensive than Glacier for long-term storage.

  Option B and D:Glacier Flexible Retrieval is costlier than Glacier Deep Archive for archival use cases with low retrieval frequency.

  References:

  AWS Storage Gateway Tape Gateway

  S3 Glacier Storage Classes

• Question 1184:

  A company is launching a new application deployed on an Amazon Elastic Container Service (Amazon ECS) cluster and is using the Fargate launch type for ECS tasks. The company is monitoring CPU and memory usage because it is expecting high traffic to the application upon its launch. However, the company wants to reduce costs when utilization decreases.

  What should a solutions architect recommend?

  A. Use Amazon EC2 Auto Scaling to scale at certain periods based on previous traffic patterns.
  B. Use an AWS Lambda function to scale Amazon ECS based on metric breaches that trigger an Amazon CloudWatch alarm.
  C. Use Amazon EC2 Auto Scaling with simple scaling policies to scale when ECS metric breaches trigger an Amazon CloudWatch alarm.
  D. Use AWS Application Auto Scaling with target tracking policies to scale when ECS metric breaches trigger an Amazon CloudWatch alarm.
  D. Use AWS Application Auto Scaling with target tracking policies to scale when ECS metric breaches trigger an Amazon CloudWatch alarm.

• Question 1185:

  A company uses Amazon S3 to store customer data that contains personally identifiable information (PII) attributes. The company needs to make the customer information available to company resources through an AWS Glue Data Catalog.

  The company needs to have fine-grained access control for the data so that only specific IAM roles can access the PII data.

  Which solution will meet these requirements?

  A. Create one IAM policy that grants access to PII. Create a second IAM policy that grants access to non-PII data. Assign the PII policy to the specified IAM roles.
  B. Create one IAM role that grants access to PII. Create a second IAM role that grants access to non-PII data. Assign the PII policy to the specified IAM roles.
  C. Use AWS Lake Formation to provide the specified IAM roles access to the PII data.
  D. Use AWS Glue to create one view for PII data. Create a second view for non-PII data. Provide the specified IAM roles access to the PII view.
  C. Use AWS Lake Formation to provide the specified IAM roles access to the PII data.

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  Explanation

  AWS Lake Formationis designed for managing fine-grained access control to data in an efficient manner:

  Granular Permissions: Lake Formation allows column-level, row-level, and table-level access controls, which can precisely define access to PII data.

  Integration with AWS Glue Catalog: Lake Formation natively integrates with AWS Glue for seamless data cataloging and access control.

  Operational Efficiency: Centralized access control policies minimize the need for separate IAM roles or policies.

  Why other options are not correct:

  Option A: Creating multiple IAM policies introduces complexity and lacks column-level access control.Not efficient.

  Option B: Managing multiple IAM roles for granular access is operationally complex.Not efficient.

  Option D: Creating views in Glue adds unnecessary complexity and may not provide the level of granularity that Lake Formation offers.Not the best choice.

  References:

  AWS Lake Formation:AWS Documentation - Lake Formation Fine-Grained Permissions with Lake Formation:AWS Documentation - Fine-Grained Permissions

• Question 1186:

  A company is running an SMB file server in its data center. The file server stores large files that are accessed frequently for the first few days after the files are created. After 7 days the files are rarely accessed. The total data size is increasing and is close to the company's total storage capacity. A solutions architect must increase the company's available storage space without losing low-latency access to the most recently accessed files. The solutions architect must also provide file lifecycle management to avoid future storage issues.

  Which solution will meet these requirements?

  A. Use AWS DataSync to copy data that is older than 7 days from the SMB file server to AWS.
  B. Create an Amazon S3 File Gateway to extend the company's storage space. Create an S3 Lifecycle policy to transition the data to S3 Glacier Deep Archive after 7 days.
  C. Create an Amazon FSx for Windows File Server file system to extend the company's storage space.
  D. Install a utility on each user's computer to access Amazon S3. Create an S3 Lifecycle policy to transition the data to S3 Glacier Flexible Retrieval after 7 days.
  B. Create an Amazon S3 File Gateway to extend the company's storage space. Create an S3 Lifecycle policy to transition the data to S3 Glacier Deep Archive after 7 days.

• Question 1187:

  A solutions architect must design a solution that uses Amazon CloudFront with an Amazon S3 origin to store a static website. The company's security policy requires that all website traffic be inspected by AWS

  WAF.

  How should the solutions architect comply with these requirements?

  A. Configure an S3 bucket policy to accept requests coming from the AWS WAF Amazon Resource Name (ARN) only.
  B. Configure Amazon CloudFront to forward all incoming requests to AWS WAF before requesting content from the S3 origin.
  C. Configure a security group that allows Amazon CloudFront IP addresses to access Amazon S3 only. Associate AWS WAF to CloudFront.
  D. Configure Amazon CloudFront and Amazon S3 to use an origin access identity (OAI) to restrict access to the S3 bucket. Enable AWS WAF on the distribution.
  D. Configure Amazon CloudFront and Amazon S3 to use an origin access identity (OAI) to restrict access to the S3 bucket. Enable AWS WAF on the distribution.

• Question 1188:

  A company is developing a latency-sensitive application. Part of the application includes several AWS Lambda functions that need to initialize as quickly as possible. The Lambda functions are written in Java and contain initialization code outside the handlers to load libraries, initialize classes, and generate unique

  IDs.

  Which solution will meet the startup performance requirement MOST cost-effectively?

  A. Move all the initialization code to the handlers for each Lambda function. Activate Lambda SnapStart for each Lambda function. Configure SnapStart to reference the $LATEST version of each Lambda function.
  B. Publish a version of each Lambda function. Create an alias for each Lambda function. Configure each alias to point to its corresponding version. Set up provisioned concurrency configuration for each Lambda function to point to the corresponding alias.
  C. Publish a version of each Lambda function. Set up a provisioned concurrency configuration for each Lambda function to point to the corresponding version. Activate Lambda SnapStart for the published versions of the Lambda functions.
  D. Update the Lambda functions to add a pre-snapshot hook. Move the code that generates unique IDs into the handlers. Publish a version of each Lambda function. Activate Lambda SnapStart for the published versions of the Lambda functions.
  D. Update the Lambda functions to add a pre-snapshot hook. Move the code that generates unique IDs into the handlers. Publish a version of each Lambda function. Activate Lambda SnapStart for the published versions of the Lambda functions.

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  Explanation

  Same scenario as above--cost-effective cold start reduction for Java Lambdas is best achieved by using Lambda SnapStart with the correct code structure. Unique IDs and similar non-deterministic logic should be in the handler or in a pre-snapshot hook. SnapStart is supported only for published versions. This combination offers cold start performance at a lower cost than provisioned concurrency.

  AWS Documentation Extract:

  " When using Lambda SnapStart for Java, ensure non-deterministic initialization code, such as unique ID generators, is executed inside the handler or in a pre-snapshot hook. SnapStart is supported only on published versions. " (Source: AWS Lambda documentation, SnapStart for Java)

  References:

  AWS Certified Solutions Architect?Official Study Guide, Lambda Performance Section.

• Question 1189:

  A company processes streaming data by using Amazon Kinesis Data Streams and an AWS Lambda function. The streaming data comes from devices that are connected to the internet. The company is experiencing scaling problems and needs to implement shard-level control and custom checkpointing.

  Which solution will meet these requirements with the LEAST latency?

  A. Connect Kinesis Data Streams to Amazon Data Firehose to ingest incoming data to an Amazon S3 bucket. Configure S3 Event Notifications to invoke the Lambda function.
  B. Increase the provisioned concurrency settings for the Lambda function. Stream the data from Kinesis Data Streams to an Amazon Simple Queue Service (Amazon SQS) standard queue. Invoke the Lambda function to process the messages.
  C. Run the Lambda function code in an Amazon Elastic Container Service (Amazon ECS) container that runs on AWS Fargate. Change the code to use the Kinesis Client Library (KCL).
  D. Increase the memory and provisioned concurrency settings for the Lambda function. Stream the data from Kinesis Data Streams to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Configure the Lambda function to be invoked by the SQS queue.
  C. Run the Lambda function code in an Amazon Elastic Container Service (Amazon ECS) container that runs on AWS Fargate. Change the code to use the Kinesis Client Library (KCL).

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  Explanation

  The requirements "shard-level control" and "custom checkpointing" point directly to the Kinesis Client Library (KCL), which is designed for building consumer applications that coordinate shard processing, perform load balancing across workers, and manage checkpoints in a durable store (commonly DynamoDB) with fine-grained control. While Lambda can consume from Kinesis Data Streams, its event source mapping abstracts shard coordination and checkpoint behavior; it is not the best fit when you

  explicitly need custom checkpointing logic and shard-level control beyond the managed integration.

  Running the existing Lambda processing logic as a long-running consumer in Amazon ECS on AWS Fargate allows the company to operate a scalable KCL-based consumer fleet without managing servers.

  With Fargate, AWS manages the underlying compute while the application maintains direct control over shard assignment and checkpoint timing/frequency through KCL--exactly what the requirement calls for.

  Latency is minimized because the consumer reads directly from Kinesis Data Streams and processes records continuously, avoiding additional buffering layers or store-and-forward patterns.

  Option A adds significant latency by delivering to S3 through Firehose and then triggering processing from S3 object notifications; this is optimized for delivery and batch-style processing, not low-latency streaming with shard-level control. Options B and D introduce SQS between Kinesis and processing, which adds another hop and does not inherently provide shard-level control or KCL-style checkpointing; FIFO also limits throughput and is not intended for high-scale streaming fan-in. Increasing Lambda provisioned concurrency can reduce cold starts, but it does not solve the need for custom checkpointing at the shard level.

  Therefore, C best meets shard-level control and custom checkpointing requirements with the least latency by using a direct KCL consumer on a managed compute platform (Fargate).

• Question 1190:

  A company copies 200 TB of data from a recent ocean survey onto AWS Snowball Edge Storage Optimized devices. The company has a high performance computing (HPC) cluster that is hosted on AWS to look for oil and gas deposits.

  A solutions architect must provide the cluster with consistent sub-millisecond latency and high-throughput access to the data on the Snowball Edge Storage Optimized devices. The company is sending the devices back to AWS.

  Which solution will meet these requirements?

  A. Create an Amazon S3 bucket. Import the data into the S3 bucket. Configure an AWS Storage Gateway file gateway to use the S3 bucket. Access the file gateway from the HPC cluster instances.
  B. Create an Amazon S3 bucket. Import the data into the S3 bucket. Configure an Amazon FSx for Lustre file system, and integrate it with the S3 bucket. Access the FSx for Lustre file system from the HPC cluster instances.
  C. Create an Amazon S3 bucket and an Amazon Elastic File System (Amazon EFS) file system. Import the data into the S3 bucket. Copy the data from the S3 bucket to the EFS file system. Access the EFS file system from the HPC cluster instances.
  D. Create an Amazon FSx for Lustre file system. Import the data directly into the FSx for Lustre file system. Access the FSx for Lustre file system from the HPC cluster instances.
  B. Create an Amazon S3 bucket. Import the data into the S3 bucket. Configure an Amazon FSx for Lustre file system, and integrate it with the S3 bucket. Access the FSx for Lustre file system from the HPC cluster instances.