Practice Free SAA-C03 Exam Online Questions
Question #111
A company is planning to run an AI/ML workload on AWS. The company needs to train a model on a dataset that is in Amazon S3 Standard. A model training application requires multiple compute nodes and single-digit millisecond access to the data.
Which solution will meet these requirements in the MOST cost-effective way?
- A . Move the data to S3 Intelligent-Tiering. Point the model training application to S3 Intelligent-Tiering as the data source.
- B . Add partitions to the S3 bucket by adding random prefixes. Reconfigure the model training application to point to the new prefixes as the data source.
- C . Move the data to S3 Express One Zone. Point the model training application to S3 Express One Zone as the data source.
- D . Move the data to a General Purpose SSD (gp3) Amazon Elastic Block Store (Amazon EBS) volume attached to an Amazon EC2 instance. Point the model training application to the gp3 volume as the data source.
Correct Answer: C
C
Explanation:
Amazon S3 Express One Zone provides single-digit millisecond latency and high throughput, making it ideal for ML workloads that require multiple compute nodes and fast access. It is also more cost-effective than traditional file or block storage for temporary, high-speed needs.
Reference: AWS Documentation C Amazon S3 Storage Classes
C
Explanation:
Amazon S3 Express One Zone provides single-digit millisecond latency and high throughput, making it ideal for ML workloads that require multiple compute nodes and fast access. It is also more cost-effective than traditional file or block storage for temporary, high-speed needs.
Reference: AWS Documentation C Amazon S3 Storage Classes
Question #112
A company currently runs an on-premises stock trading application by using Microsoft Windows Server. The company wants to migrate the application to the AWS Cloud. The company needs to design a highly available solution that provides low-latency access to block storage across multiple Availability Zones.
Which solution will meet these requirements with the LEAST implementation effort?
- A . Configure a Windows Server cluster that spans two Availability Zones on Amazon EC2 instances. Install the application on both cluster nodes. Use Amazon FSx for Windows File Server as shared storage between the two cluster nodes.
- B . Configure a Windows Server cluster that spans two Availability Zones on Amazon EC2 instances. Install the application on both cluster nodes Use Amazon Elastic Block Store (Amazon EBS) General Purpose SSD (gp3) volumes as storage attached to the EC2 instances. Set up application-level replication to sync data from one EBS volume in one Availability Zone to another EBS volume in the second Availability Zone.
- C . Deploy the application on Amazon EC2 instances in two Availability Zones Configure one EC2 instance as active and the second EC2 instance in standby mode. Use an Amazon FSx for NetApp ONTAP Multi-AZ file system to access the data by using Internet Small Computer Systems Interface (iSCSI) protocol.
- D . Deploy the application on Amazon EC2 instances in two Availability Zones. Configure one EC2 instance as active and the second EC2 instance in standby mode. Use Amazon Elastic Block Store (Amazon EBS) Provisioned IOPS SSD (io2) volumes as storage attached to the EC2 instances. Set up Amazon EBS level replication to sync data from one io2 volume in one Availability Zone to another io2 volume in the second Availability Zone.
Correct Answer: A
A
Explanation:
This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.
Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.
Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.
Why Not Other Options?:
Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.
Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.
Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort. AWS
Reference: Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.
Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.
A
Explanation:
This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.
Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.
Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.
Why Not Other Options?:
Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.
Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.
Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort. AWS
Reference: Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.
Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.
Question #113
A company needs a solution to ingest streaming sensor data from 100, 000 devices, transform the data in near real time, and load the data into Amazon S3 for analysis. The solution must be fully managed, scalable, and maintain sub-second ingestion latency.
- A . Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache Flink to process the data in near real time. Use an Amazon Data Firehose stream to send processed data to Amazon S3.
- B . Use Amazon Simple Queue Service (Amazon SQS) standard queues to collect the sensor data. Invoke AWS Lambda functions to transform and process SQS messages in batches. Configure the Lambda functions to use an AWS SDK to write transformed data to Amazon S3.
- C . Deploy a fleet of Amazon EC2 instances that run Apache Kafka to ingest the data. Run Apache Spark on Amazon EMR clusters to process the data. Configure Spark to write processed data directly to Amazon S3.
- D . Implement Amazon EventBridge to capture all sensor data. Use AWS Batch to run containerized transformation jobs on a schedule. Configure AWS Batch jobs to process data in chunks. Save results
to Amazon S3.
Correct Answer: A
A
Explanation:
The most scalable and managed solution for streaming ingestion, real-time transformation, and
delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.
From AWS Documentation:
“Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3.” (Source: Amazon Kinesis Developer Guide)
Why A is correct:
Fully managed: All services involved are serverless and managed. Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high-throughput workloads like 100, 000+ devices.
Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling.
Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup capability.
Why other options are incorrect:
Option B: SQS is not optimized for real-time streaming at high volume.
Option C: EC2 + Kafka + EMR adds high operational overhead and cost.
Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is
unsuitable for near real-time processing.
Reference: Amazon Kinesis Developer Guide
AWS Well-Architected Framework C Performance Efficiency Pillar Amazon Managed Flink (Apache Flink on KDA)
A
Explanation:
The most scalable and managed solution for streaming ingestion, real-time transformation, and
delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.
From AWS Documentation:
“Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3.” (Source: Amazon Kinesis Developer Guide)
Why A is correct:
Fully managed: All services involved are serverless and managed. Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high-throughput workloads like 100, 000+ devices.
Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling.
Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup capability.
Why other options are incorrect:
Option B: SQS is not optimized for real-time streaming at high volume.
Option C: EC2 + Kafka + EMR adds high operational overhead and cost.
Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is
unsuitable for near real-time processing.
Reference: Amazon Kinesis Developer Guide
AWS Well-Architected Framework C Performance Efficiency Pillar Amazon Managed Flink (Apache Flink on KDA)
Question #114
A finance company is migrating its trading platform to AWS. The trading platform processes a high volume of market data and processes stock trades. The company needs to establish a consistent, low-latency network connection from its on-premises data center to AWS.
The company will host resources in a VPC. The solution must not use the public internet.
Which solution will meet these requirements?
- A . Use AWS Client VPN to connect the on-premises data center to AWS.
- B . Use AWS Direct Connect to set up a connection from the on-premises data center to AWS
- C . Use AWS Private Link to set up a connection from the on-premises data center to AWS.
- D . Use AWS Site-to-Site VPN to connect the on-premises data center to AWS.
Correct Answer: B
B
Explanation:
AWS Direct Connect is the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades. Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn’t traverse the public internet, which enhances both security and performance consistency. AWS
Reference: AWS Direct Connect provides a dedicated network connection to AWS services with consistent, low-latency performance.
Best Practices for High Performance on AWS for performance-sensitive workloads like trading platforms.
Why the other options are incorrect:
B
Explanation:
AWS Direct Connect is the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades. Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn’t traverse the public internet, which enhances both security and performance consistency. AWS
Reference: AWS Direct Connect provides a dedicated network connection to AWS services with consistent, low-latency performance.
Best Practices for High Performance on AWS for performance-sensitive workloads like trading platforms.
Why the other options are incorrect:
Question #115
A company maintains its accounting records in a custom application that runs on Amazon EC2 instances. The company needs to migrate the data to an AWS managed service for development and maintenance of the application data. The solution must require minimal operational support and provide immutable, cryptographically verifiable logs of data changes.
Which solution will meet these requirements MOST cost-effectively?
- A . Copy the records from the application into an Amazon Redshift cluster.
- B . Copy the records from the application into an Amazon Neptune cluster.
- C . Copy the records from the application into an Amazon Timestream database.
- D . Copy the records from the application into an Amazon Quantum Ledger Database (Amazon QLDB) ledger.
Correct Answer: D
D
Explanation:
Amazon QLDB is the most cost-effective and suitable service for maintaining immutable, cryptographically verifiable logs of data changes. QLDB provides a fully managed ledger database with a built-in cryptographic hash chain, making it ideal for recording changes to accounting records, ensuring data integrity and security.
QLDB reduces operational overhead by offering fully managed services, so there’s no need for server management, and it’s built specifically to ensure immutability and verifiability, making it the best fit for the given requirements.
Option A (Redshift): Redshift is designed for analytics and not for immutable, cryptographically verifiable logs.
Option B (Neptune): Neptune is a graph database, which is not suitable for this use case.
Option C (Timestream): Timestream is a time series database optimized for time-stamped data, but it does not provide immutable or cryptographically verifiable logs. AWS
Reference: Amazon QLDB
How QLDB Works
D
Explanation:
Amazon QLDB is the most cost-effective and suitable service for maintaining immutable, cryptographically verifiable logs of data changes. QLDB provides a fully managed ledger database with a built-in cryptographic hash chain, making it ideal for recording changes to accounting records, ensuring data integrity and security.
QLDB reduces operational overhead by offering fully managed services, so there’s no need for server management, and it’s built specifically to ensure immutability and verifiability, making it the best fit for the given requirements.
Option A (Redshift): Redshift is designed for analytics and not for immutable, cryptographically verifiable logs.
Option B (Neptune): Neptune is a graph database, which is not suitable for this use case.
Option C (Timestream): Timestream is a time series database optimized for time-stamped data, but it does not provide immutable or cryptographically verifiable logs. AWS
Reference: Amazon QLDB
How QLDB Works
Question #116
A company runs an order management application on AWS. The application allows customers to place orders and pay with a credit card. The company uses an Amazon CloudFront distribution to deliver the application.
A security team has set up logging for all incoming requests. The security team needs a solution to generate an alert if any user modifies the logging configuration. (Select TWO):
- A . Configure an Amazon EventBridge rule that is invoked when a user creates or modifies a CloudFront distribution. Add the AWS Lambda function as a target of the EventBridge rule.
- B . Create an Application Load Balancer (ALB). Enable AWS WAF rules for the ALB. Configure an AWS Config rule to detect security violations.
- C . Create an AWS Lambda function to detect changes in CloudFront distribution logging. Configure the Lambda function to use Amazon Simple Notification Service (Amazon SNS) to send notifications to the security team.
- D . Set up Amazon GuardDuty. Configure GuardDuty to monitor findings from the CloudFront distribution. Create an AWS Lambda function to address the findings.
- E . Create a private API in Amazon API Gateway. Use AWS WAF rules to protect the private API from common security problems.
Correct Answer: A, C
A, C
Explanation:
Detailed
A, C
Explanation:
Detailed
Question #117
How can DynamoDB data be made available for long-term analytics with minimal operational overhead?
- A . Configure DynamoDB incremental exports to S3.
- B . Configure DynamoDB Streams to write records to S3.
- C . Configure EMR to copy DynamoDB data to S3.
- D . Configure EMR to copy DynamoDB data to HDFS.
Correct Answer: A
A
Explanation:
Option A is the most automated and cost-efficient solution for exporting data to S3 for analytics.
Option B involves manual setup of Streams to S3.
Options C and D introduce complexity with EMR.
A
Explanation:
Option A is the most automated and cost-efficient solution for exporting data to S3 for analytics.
Option B involves manual setup of Streams to S3.
Options C and D introduce complexity with EMR.
Question #118
An ecommerce company is redesigning a product catalog system to handle millions of products and provide fast access to product information. The system needs to store structured product data such as product name, price, description, and category. The system also needs to store unstructured data such as high-resolution product videos and user manuals. The architecture must be highly available and must be able to handle sudden spikes in traffic during large-scale sales events.
- A . Use an Amazon RDS Multi-AZ deployment to store product information. Store product videos and user manuals in Amazon S3.
- B . Use Amazon DynamoDB to store product information. Store product videos and user manuals in Amazon S3.
- C . Store all product information, including product videos and user manuals, in Amazon DynamoDB.
- D . Deploy an Amazon Document DB (with MongoDB compatibility) cluster to store all product information, product videos, and user manuals.
Correct Answer: B
B
Explanation:
Amazon DynamoDB provides single-digit millisecond performance at any scale and is fully managed to handle millions of catalog records. It is ideal for structured catalog data such as product metadata and scales seamlessly during high-traffic events like sales. Amazon S3 is optimized for storing unstructured large objects such as videos and manuals, with virtually unlimited scalability and high durability.
Option A (RDS) would not handle massive scale or traffic spikes as efficiently.
Option C overloads DynamoDB by forcing it to store large binary data, which is not its purpose.
Option D (Document DB) is suitable for JSON-like documents but not optimal for storing large media files and would add operational complexity. Therefore, option B represents the best separation of structured and unstructured data storage.
Reference:
• DynamoDB Developer Guide ― Millisecond performance at scale
• Amazon S3 User Guide ― Storage for unstructured data
• AWS Well-Architected Framework ― Performance Efficiency Pillar
B
Explanation:
Amazon DynamoDB provides single-digit millisecond performance at any scale and is fully managed to handle millions of catalog records. It is ideal for structured catalog data such as product metadata and scales seamlessly during high-traffic events like sales. Amazon S3 is optimized for storing unstructured large objects such as videos and manuals, with virtually unlimited scalability and high durability.
Option A (RDS) would not handle massive scale or traffic spikes as efficiently.
Option C overloads DynamoDB by forcing it to store large binary data, which is not its purpose.
Option D (Document DB) is suitable for JSON-like documents but not optimal for storing large media files and would add operational complexity. Therefore, option B represents the best separation of structured and unstructured data storage.
Reference:
• DynamoDB Developer Guide ― Millisecond performance at scale
• Amazon S3 User Guide ― Storage for unstructured data
• AWS Well-Architected Framework ― Performance Efficiency Pillar
Question #119
A company uses Amazon Redshift to store structured data and Amazon S3 to store unstructured data. The company wants to analyze the stored data and create business intelligence reports. The company needs a data visualization solution that is compatible with Amazon Redshift and Amazon S3.
Which solution will meet these requirements?
- A . Use Amazon Redshift query editor v2 to analyze data stored in Amazon Redshift. Use Amazon Athena to analyze data stored in Amazon S3. Use Amazon QuickSight to access Amazon Redshift and Athena, visualize the data analyses, and create business intelligence reports.
- B . Use Amazon Redshift Serverless to analyze data stored in Amazon Redshift. Use Amazon S3 Object Lambda to analyze data stored in Amazon S3. Use Amazon Managed Grafana to access Amazon Redshift and Object Lambda, visualize the data analyses, and create business intelligence reports.
- C . Use Amazon Redshift Spectrum to analyze data stored in Amazon Redshift. Use Amazon Athena to analyze data stored in Amazon S3. Use Amazon QuickSight to access Amazon Redshift and Athena, visualize the data analyses, and create business intelligence reports.
- D . Use Amazon OpenSearch Service to analyze data stored in Amazon Redshift and Amazon S3. Use Amazon Managed Grafana to access OpenSearch Service, visualize the data analyses, and create business intelligence reports.
Correct Answer: C
C
Explanation:
This solution leverages:
Amazon Redshift Spectrum to query S3 data directly from Redshift.
Amazon Athena for ad-hoc analysis of S3 data.
Amazon QuickSight for unified visualization from multiple data sources.
“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”
“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”
― Redshift Spectrum
― Amazon QuickSight Supported Data Sources
This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI
dashboards.
Incorrect Options:
A: The query editor is not a BI tool.
B, D: Grafana is better for time-series data, not structured analytics or BI reports.
Reference: Redshift Spectrum
Amazon QuickSight Integration
C
Explanation:
This solution leverages:
Amazon Redshift Spectrum to query S3 data directly from Redshift.
Amazon Athena for ad-hoc analysis of S3 data.
Amazon QuickSight for unified visualization from multiple data sources.
“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”
“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”
― Redshift Spectrum
― Amazon QuickSight Supported Data Sources
This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI
dashboards.
Incorrect Options:
A: The query editor is not a BI tool.
B, D: Grafana is better for time-series data, not structured analytics or BI reports.
Reference: Redshift Spectrum
Amazon QuickSight Integration
Question #120
A company is testing an application that runs on an Amazon EC2 Linux instance. A single 500 GB Amazon Elastic Block Store (Amazon EBS) General Purpose SSD (gp2) volume is attached to the EC2 instance.
The company will deploy the application on multiple EC2 instances in an Auto Scaling group. All instances require access to the data that is stored in the EBS volume. The company needs a highly available and resilient solution that does not introduce significant changes to the application’s code.
Which solution will meet these requirements?
- A . Provision an EC2 instance that uses NFS server software. Attach a single 500 GB gp2 EBS volume to the instance.
- B . Provision an Amazon FSx for Windows File Server file system. Configure the file system as an SMB file store within a single Availability Zone.
- C . Provision an EC2 instance with two 250 GB Provisioned IOPS SSD EBS volumes.
- D . Provision an Amazon Elastic File System (Amazon EFS) file system. Configure the file system to use General Purpose performance mode.
Correct Answer: D
D
Explanation:
Amazon EFS is a fully managed, scalable file storage service that can be accessed concurrently by thousands of EC2 instances. It supports General Purpose performance mode for latency-sensitive use cases and provides high availability and durability across multiple Availability Zones with minimal
changes to application code.
Reference: AWS Documentation C Amazon Elastic File System (EFS)
D
Explanation:
Amazon EFS is a fully managed, scalable file storage service that can be accessed concurrently by thousands of EC2 instances. It supports General Purpose performance mode for latency-sensitive use cases and provides high availability and durability across multiple Availability Zones with minimal
changes to application code.
Reference: AWS Documentation C Amazon Elastic File System (EFS)