Amazon AWS Certified Solutions Architect – Associate SAA-C03 Exam Dumps and Practice Test Questions Set 8 Q106- 120

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Question 106:

A company wants to deploy a web application across multiple Availability Zones to ensure high availability. The application must automatically scale based on traffic patterns. Which architecture is most suitable?
A) Auto Scaling group across multiple Availability Zones behind an Application Load Balancer
B) Single EC2 instance in one Availability Zone with manual scaling
C) EC2 instances in one Availability Zone behind a Network Load Balancer
D) Amazon Lightsail instance with periodic snapshots

Answer:

A) Auto Scaling group across multiple Availability Zones behind an Application Load Balancer

Explanation:

In this scenario, the company needs to deploy a web application across multiple Availability Zones to ensure high availability, while also enabling automatic scaling in response to fluctuating traffic patterns. The most appropriate architecture for these requirements is an Auto Scaling group across multiple Availability Zones behind an Application Load Balancer (ALB). This combination addresses both the need for fault tolerance and the need to dynamically adjust resources to match user demand, providing a highly resilient and scalable solution.

An Auto Scaling group (ASG) is a core AWS service that automatically manages the number of EC2 instances in response to defined metrics such as CPU utilization, memory usage, or custom application-level metrics. When traffic increases, the Auto Scaling group launches additional instances to handle the load, maintaining application performance and minimizing response time. Conversely, when traffic decreases, it terminates unneeded instances to reduce operational costs. This automatic scaling mechanism ensures that the application can handle unpredictable traffic patterns without manual intervention, which is critical for web applications experiencing fluctuating or unpredictable workloads.

Deploying instances across multiple Availability Zones adds a critical layer of fault tolerance. Each Availability Zone is a physically isolated data center within an AWS region, with independent power, networking, and cooling. By placing instances in multiple AZs, the architecture protects against localized failures such as hardware malfunctions, network interruptions, or even an entire AZ outage. In the event of an AZ failure, the Auto Scaling group automatically redistributes traffic to instances in the remaining healthy zones, ensuring continuous availability of the application. This multi-AZ deployment is an essential design pattern for building highly available applications in AWS and aligns with best practices for resilient architecture.

The Application Load Balancer (ALB) serves as a central component for traffic distribution. It routes incoming requests intelligently across multiple instances and Availability Zones, ensuring that no single instance becomes a bottleneck. ALB also performs health checks on each instance, automatically removing unhealthy targets from the routing pool and sending traffic only to healthy instances. This guarantees that users are not directed to failed or degraded instances. Additional features of ALB, such as SSL/TLS termination, path-based routing, and sticky sessions, further enhance security, application performance, and user experience. By combining ALB with an Auto Scaling group, the architecture achieves both elasticity and resilience, ensuring the application remains responsive even under heavy or unpredictable loads.

Option B, a single EC2 instance in one Availability Zone with manual scaling, introduces a single point of failure. If the instance or its AZ fails, the application will experience downtime, and manual intervention is required to restore service. This approach is insufficient for applications that demand high availability and cannot efficiently handle spikes in traffic without risking performance degradation.

Option C, EC2 instances in one Availability Zone behind a Network Load Balancer (NLB), provides high-performance, low-latency network-level load balancing for TCP and UDP traffic. However, deploying instances in a single AZ exposes the application to downtime if that AZ fails. While the NLB ensures efficient traffic distribution at the network layer, it does not inherently provide automatic scaling or multi-AZ fault tolerance, which are critical for high availability and elastic resource management.

Option D, Amazon Lightsail with periodic snapshots, is designed for simpler workloads with predictable traffic. Lightsail provides an easy setup but lacks advanced scaling features and automatic failover across multiple Availability Zones. Snapshots can assist with disaster recovery but do not prevent downtime during a failure, making it unsuitable for high-traffic or mission-critical web applications.

By implementing an Auto Scaling group across multiple Availability Zones with an Application Load Balancer, the company ensures a highly available, fault-tolerant, and dynamically scalable architecture. The Auto Scaling group adjusts compute capacity based on traffic demand, while the ALB intelligently distributes traffic and monitors instance health. This combination reduces operational overhead, improves application performance, and ensures continuous availability for end users.

Question 107:

A company runs a containerized application on ECS Fargate. Microservices require secure access to API keys and database credentials, with encryption and automatic rotation. Which service should be used?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS

Answer:

A) AWS Secrets Manager

Explanation:

AWS Secrets Manager is designed to securely store API keys, passwords, and other sensitive information. Secrets are encrypted using AWS KMS and can be rotated automatically, reducing operational overhead and ensuring compliance.

ECS Fargate tasks can retrieve secrets programmatically at runtime. Fine-grained IAM policies ensure that each microservice can access only the secrets it is authorized to use. CloudTrail auditing provides monitoring and compliance visibility.

Option B, RDS Parameter Groups, manage only database configurations and cannot handle general secrets. Option C, EC2 Instance Metadata, is unavailable for Fargate tasks. Option D, Amazon EFS, is a shared file system and does not provide encryption, automatic rotation, or fine-grained access control.

This solution follows AWS security best practices for containerized workloads and aligns with SAA-C03 exam objectives regarding secure, automated secret management.

Question 108:

A company wants to analyze large volumes of semi-structured log data stored in S3 without building ETL pipelines. Which service is most appropriate?
A) Amazon Athena
B) Amazon EMR
C) Amazon Redshift
D) AWS Glue

Answer:

A) Amazon Athena

Explanation:

In this scenario, the company needs to analyze large volumes of semi-structured log data stored in Amazon S3 without building ETL pipelines. The focus is on querying data directly in its raw format, using standard SQL queries, without the overhead of provisioning servers, managing clusters, or performing data transformations. The most appropriate service for this requirement is Amazon Athena, a serverless query service designed specifically for interactive, on-demand analysis of data stored in S3.

Amazon Athena allows users to run SQL queries directly on S3 data, providing a highly flexible and cost-efficient way to analyze large datasets. Because Athena is serverless, there is no need to provision or manage servers, clusters, or infrastructure. This means the company can start querying immediately after defining the schema for its data. Serverless architecture also ensures that the service automatically scales to handle multiple concurrent queries, allowing several analysts or applications to work with the data simultaneously. The company benefits from on-demand querying without the complexity or cost associated with maintaining compute resources.

One of the key advantages of Athena is its schema-on-read capability, which allows users to define how data should be interpreted at the time of the query rather than requiring the data to be transformed or loaded into a structured database ahead of time. This is particularly important for semi-structured log data, which is often stored in formats such as JSON, CSV, Parquet, or ORC. Athena natively supports these formats, enabling users to query complex log datasets directly without preprocessing. This eliminates the need for traditional ETL pipelines, significantly reducing development effort and operational overhead while enabling faster access to insights.

Athena integrates seamlessly with the AWS Glue Data Catalog, which serves as a centralized metadata repository. The Glue Data Catalog allows users to define table schemas, partitions, and column data types, which improves query performance and provides consistency across teams and applications. Partitioning log data in S3 allows Athena to scan only the relevant subset of data for each query, improving performance and reducing cost because Athena charges based on the amount of data scanned. For large volumes of log data, this can lead to significant cost savings while maintaining high performance for analytics workloads.

Option B, Amazon EMR, is a managed big data platform that supports frameworks such as Hadoop, Spark, Hive, and Presto. EMR is well-suited for batch processing, distributed data transformations, and large-scale analytics, but it requires the provisioning and management of clusters, configuration of nodes, and maintenance of the compute infrastructure. For a scenario that requires ad hoc, SQL-based querying without ETL pipelines, EMR introduces unnecessary operational complexity and is less efficient for direct analysis of S3-stored log data. EMR is more appropriate when the workload involves large-scale batch processing or machine learning pipelines.

Option C, Amazon Redshift, is a fully managed data warehouse that excels at analyzing structured datasets with high performance. However, Redshift requires data to be loaded into tables within the warehouse, which means an ETL or data ingestion process must be performed before queries can be executed. This approach does not satisfy the requirement of querying log data directly without building ETL pipelines. Redshift is ideal for structured analytics and recurring reporting but is less flexible for interactive, ad hoc analysis of raw log data.

Option D, AWS Glue, is primarily an ETL service that helps extract, transform, and load data from various sources into structured storage for analysis. While Glue can catalog metadata and transform datasets, it does not provide interactive SQL querying directly on raw S3 data. Glue is intended for data preparation and pipeline orchestration rather than immediate analysis of semi-structured log data without transformation.

By using Amazon Athena, the company gains a serverless, fully managed, and scalable solution for log data analytics. It supports multiple data formats, allows ad hoc queries without provisioning infrastructure, and integrates with the Glue Data Catalog for schema management and partitioning. Athena automatically handles query execution and scaling, providing fast and cost-efficient analysis of large volumes of data

Question 109:

A company processes millions of IoT telemetry events per second. Multiple applications must consume the same stream concurrently with durability and low latency. Which service is most suitable?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ

Answer:

A) Amazon Kinesis Data Streams

Explanation:

Kinesis Data Streams is designed for real-time, high-throughput streaming data. Data is divided into shards, allowing multiple consumers to read concurrently. Enhanced fan-out provides each consumer dedicated throughput, reducing latency.

Data is replicated across Availability Zones for durability. Integration with AWS Lambda or analytics services allows serverless, event-driven processing. Kinesis scales horizontally to handle millions of events per second.

Option B, SQS, does not efficiently support multiple consumers reading the same message simultaneously. Option C, SNS, lacks replay and high-throughput optimization. Option D, Amazon MQ, is a traditional broker not optimized for massive low-latency streaming.

This solution provides real-time, durable, and scalable ingestion, aligning with SAA-C03 exam objectives for high-volume streaming architectures.

Question 110:

A company wants to deploy a multi-tier web application with a highly available database and caching layer. Automatic failover must occur if the primary database fails. Which configuration is most suitable?
A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache
B) Single RDS instance with snapshots and caching
C) RDS read replicas only
D) Self-managed EC2 database with replication

Answer:

A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache

Explanation:

In this scenario, the company needs to deploy a multi-tier web application with requirements for a highly available database and a caching layer that enhances performance. The solution must ensure automatic failover in the event the primary database fails. The most suitable configuration for this scenario is an Amazon RDS Multi-AZ deployment combined with Amazon ElastiCache, which provides a robust, fault-tolerant, and high-performance architecture.

Amazon RDS Multi-AZ deployments are designed to provide high availability and resilience for relational databases. When Multi-AZ is enabled, RDS automatically provisions a synchronous standby instance in a different Availability Zone from the primary database. Data is continuously replicated to this standby instance, ensuring consistency and durability. If the primary instance fails due to hardware issues, network interruptions, or an Availability Zone outage, Amazon RDS automatically fails over to the standby instance with minimal downtime. This failover process is fully managed by AWS, reducing operational complexity and ensuring that the application remains available to end users.

High availability is critical in multi-tier architectures because the database layer often acts as the backbone of the application. Any downtime in the database can cause the entire application to become unresponsive. By deploying RDS across multiple Availability Zones, the application can continue functioning even if one AZ fails. Routine maintenance, such as patching or upgrades, is also managed seamlessly with Multi-AZ, as updates can be applied to the standby before promotion, further minimizing disruption. This design meets enterprise requirements for mission-critical workloads that demand continuous uptime.

Amazon ElastiCache complements RDS by providing a managed in-memory caching layer that reduces database load and improves application performance. Frequently accessed data, such as session information, query results, or other repeated data, can be stored in the cache to provide rapid retrieval. This decreases latency for users and allows the database to focus on write-intensive operations, improving scalability. By offloading read-heavy workloads to ElastiCache, applications can handle more concurrent requests without sacrificing performance, which is particularly important for high-traffic multi-tier applications.

Option B, a single RDS instance with snapshots and caching, provides limited high availability. Snapshots allow for backup and recovery, but restoring from a snapshot requires manual intervention and results in extended downtime. Although ElastiCache can improve performance, the lack of automatic failover makes this option unsuitable for scenarios that require continuous availability.

Option C, RDS read replicas only, is intended primarily for read scaling. Read replicas can handle read-heavy workloads but do not automatically replace a failed primary database. Promotion of a read replica to primary requires manual intervention, which increases the risk of downtime. While read replicas are useful for scaling reads, they do not meet the requirement for automated high availability and failover.

Option D, a self-managed database on EC2 with replication, offers flexibility but introduces significant operational overhead. Administrators must manage replication, backups, failover procedures, patching, and monitoring. Any misconfiguration or human error could lead to downtime or data loss. While technically feasible, it is not as reliable or efficient as a fully managed Multi-AZ deployment.

By deploying Amazon RDS Multi-AZ with Amazon ElastiCache, the company achieves a resilient, high-performing, and highly available architecture. Automatic failover ensures minimal disruption during database failures, while ElastiCache improves application response times and reduces database load. This combination reduces operational complexity, improves reliability, and enhances user experience.

Question 111:

A company wants to deploy a web application globally with low latency. Static content is stored in Amazon S3, and dynamic content is served by EC2 instances in multiple regions. Which architecture ensures low latency, high availability, and secure access to S3?
A) Amazon CloudFront with S3 origin and regional EC2 origin failover
B) Public S3 bucket with HTTPS
C) Amazon SNS with cross-region replication
D) Amazon Global Accelerator with a single EC2 origin

Answer:

A) Amazon CloudFront with S3 origin and regional EC2 origin failover

Explanation:

Amazon CloudFront is a global content delivery network (CDN) that caches content at edge locations close to users. This significantly reduces latency by serving content from the nearest location. Using S3 as the origin for static content ensures scalable, durable, and secure storage. CloudFront Origin Access Control prevents users from bypassing CloudFront and directly accessing S3, which enhances security.

For dynamic content, EC2 instances deployed in multiple regions provide failover capability. CloudFront can be configured with multiple origins and health checks so that if one EC2 region fails, traffic is automatically rerouted to healthy regions. HTTPS ensures secure transmission, and integration with AWS WAF provides protection against common web exploits and DDoS attacks.

Option B, using a public S3 bucket, exposes the content to the public internet and lacks caching, resulting in higher latency. Option C, SNS, is a messaging service and cannot serve dynamic or static web content. Option D, Global Accelerator with a single EC2 origin, optimizes network-level routing but does not cache static content and lacks redundancy if the EC2 origin fails.

This solution provides a globally distributed, highly available, and secure architecture, aligning with SAA-C03 objectives for web application deployment and content delivery.

Question 112:

A company processes high-volume IoT telemetry events. Multiple applications need concurrent access to the same stream with durability and low latency. Which service should be used?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ

Answer:

A) Amazon Kinesis Data Streams

Explanation:

Kinesis Data Streams is designed for real-time streaming data ingestion and processing. Data is divided into shards, allowing multiple applications to read concurrently. Enhanced fan-out provides each consumer with dedicated throughput, ensuring low latency and consistent performance.

Kinesis replicates data across multiple Availability Zones for durability, ensuring no data loss in case of a failure. Integration with AWS Lambda and analytics services enables serverless, event-driven processing pipelines. Horizontal scaling allows Kinesis to handle millions of events per second efficiently.

Option B, SQS, is primarily for decoupling and does not efficiently allow multiple consumers to read the same message simultaneously. Option C, SNS, is a pub/sub messaging service without replay capabilities or high throughput optimization. Option D, Amazon MQ, is a traditional message broker that requires manual scaling and is not optimized for low-latency, high-volume streaming.

This architecture meets real-time, durable, and scalable ingestion requirements, which is a key SAA-C03 topic for event-driven and IoT workloads.

Question 113:

A company runs a containerized application on ECS Fargate. Microservices need secure access to API keys and database credentials with encryption and automatic rotation. Which AWS service is recommended?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS

Answer:

A) AWS Secrets Manager

Explanation:

In this scenario, the company is running a containerized application on ECS Fargate where multiple microservices require secure access to sensitive credentials, such as API keys and database passwords. The solution must provide encryption, fine-grained access control, and automatic rotation to minimize operational overhead and enhance security. The recommended AWS service for this use case is AWS Secrets Manager.

AWS Secrets Manager is a fully managed service that securely stores and manages sensitive information. It encrypts secrets at rest using AWS Key Management Service (KMS), ensuring that credentials are protected from unauthorized access. Encryption is applied automatically, and users can manage keys according to organizational security policies. Secrets Manager also supports automatic rotation for supported databases and custom secrets, allowing credentials to be periodically changed without manual intervention. This feature minimizes the risk of compromised credentials and ensures that secrets are regularly updated according to security best practices.

In an ECS Fargate environment, IAM roles for tasks can be used to control access to specific secrets. Each microservice can be assigned a role that permits access only to the secrets it is authorized to use. This enforces the principle of least privilege, ensuring that no microservice can access credentials intended for another service. Secrets are retrieved programmatically at runtime, which eliminates the need for hard-coded credentials in container images, environment variables, or source code, reducing the likelihood of accidental exposure and improving overall application security.

Secrets Manager integrates with AWS CloudTrail, providing auditing and compliance capabilities. This allows the company to monitor who accessed which secret and when, helping meet regulatory requirements and providing visibility for security monitoring. In addition, integration with Amazon CloudWatch enables monitoring of rotation schedules, detection of failures, and alerting if there are issues, ensuring that secret management processes remain reliable and automated.

Option B, Amazon RDS Parameter Groups, is designed to manage database configuration settings rather than storing arbitrary secrets. While it can store database parameters, it does not provide encryption, fine-grained access control for individual microservices, or automated rotation of credentials. Therefore, it does not satisfy the requirement for secure, flexible, and dynamic secret management in a containerized environment.

Option C, EC2 Instance Metadata, provides temporary credentials and configuration information for EC2 instances. However, ECS Fargate tasks do not have access to instance metadata, making this approach infeasible for a containerized, serverless compute environment. Additionally, instance metadata does not provide rotation or granular access control for multiple secrets.

Option D, Amazon EFS, is a managed file system that can be shared across compute resources. While EFS provides persistent storage, it lacks the ability to encrypt secrets by default, enforce access policies per microservice, or automatically rotate credentials. Using EFS for secret storage would require additional operational effort and custom automation, making it less secure and more error-prone.

By using AWS Secrets Manager, the company achieves a secure, fully managed, and scalable solution for storing and accessing sensitive credentials. It ensures encryption, automatic rotation, fine-grained access control, and auditing—all critical components for securing a containerized microservices architecture. Secrets Manager eliminates the need to hard-code secrets, reduces administrative overhead, and ensures compliance with security policies.

Question 114:

A company wants to analyze large volumes of log data in S3 without building ETL pipelines. Which service is most appropriate?
A) Amazon Athena
B) Amazon EMR
C) Amazon Redshift
D) AWS Glue

Answer:

A) Amazon Athena

Explanation:

Amazon Athena is a serverless service that allows direct SQL queries on data stored in S3. Athena supports structured and semi-structured formats like JSON, Parquet, and ORC, making it ideal for log analysis. It eliminates the need for cluster management and automatically scales to handle multiple concurrent queries.

Integration with AWS Glue Data Catalog allows for schema management, partitioning, and metadata discovery, optimizing query performance. Costs are based on the volume of data scanned rather than provisioned resources, providing a cost-efficient approach.

Option B, EMR, requires cluster management and adds operational overhead. Option C, Redshift, requires data loading and cluster provisioning. Option D, Glue, is primarily an ETL service and does not support ad hoc querying directly on S3.

Athena provides a fully managed, serverless, and scalable solution for ad hoc analytics, which aligns with SAA-C03 objectives for query-on-demand and serverless analytics.

Question 115:

Answer:

A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache

Explanation:

In this scenario, the company is deploying a multi-tier web application that requires a highly available database and a caching layer to ensure performance and low latency. The solution must also provide automatic failover in case the primary database becomes unavailable. The most appropriate configuration for these requirements is an Amazon RDS Multi-AZ deployment combined with Amazon ElastiCache. This architecture provides both resilience and performance while minimizing operational overhead.

Amazon RDS Multi-AZ deployments are designed to deliver high availability and fault tolerance for relational databases. When enabled, Multi-AZ automatically provisions a synchronous standby instance in a separate Availability Zone from the primary database. All data changes on the primary instance are synchronously replicated to the standby, ensuring data consistency and durability. In the event of a failure, whether due to hardware issues, network interruptions, or an entire Availability Zone outage, Amazon RDS automatically fails over to the standby instance, minimizing downtime. This failover process is fully managed by AWS, which reduces administrative effort and ensures that the application remains available to end users without requiring manual intervention.

Deploying RDS across multiple Availability Zones is critical in multi-tier architectures, as the database typically forms the backbone of the application. If the database were to fail without a high-availability setup, the application could experience significant downtime, affecting user experience and potentially business continuity. Multi-AZ deployments also facilitate maintenance operations such as patching and upgrades with minimal disruption. AWS can perform these operations on the standby instance first and then promote it, ensuring continuous availability and reducing operational risk.

Amazon ElastiCache complements the database layer by providing a managed in-memory caching solution. It stores frequently accessed data such as session states, query results, and computed data in memory, which allows applications to retrieve information much faster than from a relational database. By offloading read-heavy workloads to the caching layer, ElastiCache reduces the load on the database, improves response times, and enhances scalability. This is particularly important for high-traffic applications where maintaining low latency is critical for performance and user satisfaction. The combination of Multi-AZ RDS and ElastiCache ensures both resilience and speed, providing a reliable and responsive environment for the application.

Option B, a single RDS instance with snapshots and caching, does not provide automatic failover. Snapshots allow for backups and recovery, but restoration is a manual process that leads to downtime. While caching improves performance, the database layer remains a single point of failure, making this approach unsuitable for applications that require high availability and resilience.

Option C, RDS read replicas only, is designed to improve read scalability. Read replicas can serve read-heavy workloads, but they cannot automatically replace a failed primary instance. Promotion to primary must be performed manually, which introduces downtime and operational complexity. Read replicas alone do not meet the requirement for automatic failover or high availability.

Option D, a self-managed EC2 database with replication, provides flexibility but introduces significant operational complexity. Administrators are responsible for replication setup, backups, failover processes, monitoring, and patching. Any misconfiguration could result in downtime or data loss. While feasible, this approach is less reliable and more error-prone than a fully managed Multi-AZ deployment.

By deploying Amazon RDS Multi-AZ with Amazon ElastiCache, the company achieves a highly available, fault-tolerant, and scalable architecture. Automatic failover ensures minimal disruption during database failures, while ElastiCache reduces database load and improves application responsiveness. This combination aligns with AWS best practices for building resilient multi-tier web applications and reduces operational overhead while maintaining performance and reliability.

Question 116:

A company wants to deploy a global web application with low latency. Static content is stored in Amazon S3, and dynamic content is generated by EC2 instances in multiple regions. Which architecture ensures low latency, high availability, and secure access to S3?
A) Amazon CloudFront with S3 origin and regional EC2 origin failover
B) Public S3 bucket with HTTPS
C) Amazon SNS with cross-region replication
D) Amazon Global Accelerator with a single EC2 origin

Answer:

A) Amazon CloudFront with S3 origin and regional EC2 origin failover

Explanation:

Amazon CloudFront is a content delivery network that caches content at edge locations close to users, reducing latency and improving performance. Static content in S3 can be secured using CloudFront Origin Access Control, preventing unauthorized access while allowing CloudFront to serve the content.

Dynamic content is served by EC2 instances in multiple regions. CloudFront can be configured with multiple origins and health checks so that traffic is routed to healthy EC2 regions if one fails. HTTPS ensures secure data transmission, while AWS WAF integration protects against common threats and DDoS attacks.

Option B exposes S3 content publicly, which is insecure and may result in higher latency without caching. Option C, SNS, is a messaging service and cannot serve web content. Option D, Global Accelerator with a single EC2 origin, improves network routing but does not cache content or provide failover for dynamic content.

This architecture provides a globally distributed, highly available, secure solution, which aligns with SAA-C03 best practices for low-latency web application deployment.

Question 117:

A company processes millions of IoT telemetry events per second. Multiple applications need concurrent access with durability and low latency. Which service is most appropriate?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ

Answer:

A) Amazon Kinesis Data Streams

Explanation:

Amazon Kinesis Data Streams is designed for real-time streaming and high-throughput workloads. Data is organized into shards, allowing multiple consumer applications to read concurrently. Enhanced fan-out ensures each consumer has dedicated throughput, minimizing latency.

Data is replicated across multiple Availability Zones, ensuring durability and resilience. Integration with Lambda and analytics services allows serverless, event-driven processing. Horizontal scaling allows Kinesis to handle millions of events per second efficiently.

Option B, SQS, does not efficiently allow multiple consumers to read the same message concurrently. Option C, SNS, lacks replay capability and high-throughput optimization. Option D, Amazon MQ, is a traditional broker and not optimized for high-volume, low-latency streaming.

This solution meets SAA-C03 requirements for durable, scalable, low-latency data processing in real-time and IoT scenarios.

Question 118:

A company runs a containerized application on ECS Fargate. Microservices require secure access to API keys and database credentials with encryption and automatic rotation. Which AWS service is recommended?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS

Answer:

A) AWS Secrets Manager

Explanation:

AWS Secrets Manager provides centralized, secure storage for credentials and secrets. Secrets are encrypted using KMS and can be rotated automatically to reduce operational overhead and maintain compliance.

ECS Fargate tasks retrieve secrets programmatically at runtime. IAM roles and fine-grained policies ensure that each microservice only accesses the secrets it is authorized to use. CloudTrail auditing tracks secret access and rotation events, providing compliance visibility.

Option B, RDS Parameter Groups, manages only database configuration parameters. Option C, EC2 Instance Metadata, is unavailable in Fargate. Option D, Amazon EFS, is a shared filesystem and does not provide encryption, rotation, or access control for secrets.

This architecture follows AWS security best practices for containerized applications and aligns with SAA-C03 exam objectives for secure and automated secret management.

Question 119:

A company wants to query large volumes of log data stored in S3 without building ETL pipelines. Which service should be used?
A) Amazon Athena
B) Amazon EMR
C) Amazon Redshift
D) AWS Glue

Answer:

A) Amazon Athena

Explanation:

Amazon Athena is a serverless, interactive query service that enables users to analyze large datasets directly in Amazon S3 using standard SQL. One of the most significant advantages of Athena is that it does not require any infrastructure provisioning or cluster management, making it a fully managed, cost-effective solution for querying log files, clickstream data, or other semi-structured datasets stored in S3. Organizations can start running queries immediately without setting up ETL pipelines or moving data into a separate analytics platform.

Athena supports a wide range of data formats, including structured formats like CSV, TSV, and Parquet, as well as semi-structured formats such as JSON and ORC. This flexibility allows organizations to store raw log data in its native format and query it on demand without performing time-consuming transformations. The service automatically scales to accommodate multiple concurrent queries, enabling multiple teams or applications to perform analysis simultaneously. This feature is particularly important for companies that need real-time insights from logs for monitoring, auditing, or troubleshooting application behavior.

Integration with the AWS Glue Data Catalog enhances Athena’s capabilities by providing centralized metadata management. The Glue Data Catalog allows users to define schemas, partition datasets, and perform automatic schema discovery. Partitioning is especially important when working with massive log datasets because it significantly reduces the amount of data scanned for each query, which improves query performance and reduces costs. For example, logs can be partitioned by date, application, or region, enabling Athena to scan only the relevant subsets of data rather than the entire dataset.

Athena’s pricing model is based on data scanned per query, which encourages cost-efficient storage and query design. Users can compress data, convert to columnar formats such as Parquet or ORC, and partition data to minimize the amount scanned. This model ensures that organizations only pay for the queries they run rather than pre-provisioning infrastructure, which is common in traditional data warehouses or big data clusters.

Option B, Amazon EMR, provides a managed Hadoop and Spark platform for large-scale data processing, but it requires provisioning and managing clusters, creating operational overhead, and often necessitates complex ETL processes to prepare data for analysis. Option C, Amazon Redshift, is a fully managed data warehouse that requires loading data from S3 into Redshift tables, involving additional storage and ETL steps, which can be time-consuming and costly. Option D, AWS Glue, is designed for ETL tasks and data preparation, not for ad hoc querying or interactive analytics on raw S3 data.

Amazon Athena also supports integration with visualization and BI tools, such as Amazon QuickSight, Tableau, or Power BI, enabling users to build dashboards and perform visual analytics directly on S3 datasets. Combined with Glue and QuickSight, organizations can establish an end-to-end, serverless analytics solution for log analysis without managing any infrastructure.

From a security and compliance perspective, Athena integrates with AWS IAM for fine-grained access control, and it can enforce encryption at rest and in transit using KMS-managed keys. Queries are auditable through CloudTrail, allowing organizations to maintain governance and monitoring over sensitive log data.

In summary, Amazon Athena provides a serverless, scalable, and cost-efficient solution for querying large volumes of log data stored in S3 without requiring ETL pipelines. Its support for multiple formats, seamless integration with Glue for metadata management, partitioning capabilities for performance optimization, and compatibility with BI tools makes it ideal for analyzing logs quickly and efficiently. This approach aligns with SAA-C03 exam objectives by demonstrating the ability to design serverless analytics solutions that are highly available, operationally simple, and cost-effective.

Question 120:

Answer:

A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache

Explanation:

For multi-tier web applications, maintaining high availability and ensuring minimal downtime during failures is critical. The database layer is often the most important component for reliability, as it stores the application’s core data and state. Amazon RDS Multi-AZ deployments are specifically designed to provide high availability by synchronously replicating the primary database instance to a standby instance in a different Availability Zone. This replication occurs in real time, ensuring that both instances maintain identical data. If the primary instance fails due to hardware issues, maintenance events, or an entire AZ outage, RDS automatically performs a failover to the standby instance with minimal downtime, typically a matter of seconds, allowing the application to continue functioning without manual intervention.

In addition to the database layer, application performance can be enhanced with a caching layer. Amazon ElastiCache provides an in-memory caching solution that can store frequently accessed data, session information, or computed results. This reduces the read and write load on the database, improves response times, and allows the database to handle more complex workloads efficiently. Combining RDS Multi-AZ with ElastiCache ensures that the web application achieves both high availability and optimized performance.

Option B, a single RDS instance with snapshots and caching, does not provide automatic failover. In this setup, if the primary database fails, a manual restore from snapshots would be required, resulting in significant downtime and operational complexity. While caching can improve performance, it does not address the reliability and fault tolerance of the database itself.

Option C, RDS read replicas, are primarily designed for scaling read operations and offloading read-heavy workloads from the primary instance. Although read replicas can provide some redundancy for read operations, they cannot automatically take over as the primary database in the event of a failure. Therefore, they do not meet the requirement for automatic failover and high availability in production environments where write operations must also be protected.

Option D, self-managed EC2 databases with custom replication, introduces operational complexity and risk. Administrators must configure replication, monitoring, backup, and failover mechanisms manually. This approach increases the likelihood of misconfigurations, potential data loss, and extended recovery times during failures. Additionally, it requires ongoing management and maintenance, which can divert resources from other critical application development tasks.

The combination of RDS Multi-AZ and ElastiCache aligns with AWS best practices for deploying resilient, scalable multi-tier applications. It reduces operational overhead by providing managed failover, automatic backups, and seamless software patching. Security and compliance are also enhanced, as RDS integrates with AWS IAM for access control, KMS for encryption at rest, and supports encryption in transit using SSL/TLS. ElastiCache similarly supports encryption and IAM-based access control, ensuring that cached data remains secure.

From an SAA-C03 exam perspective, this configuration demonstrates knowledge of high availability patterns, fault-tolerant architectures, and performance optimization techniques for multi-tier applications. It highlights the ability to combine managed AWS services to create a solution that is reliable, performant, and operationally efficient, while reducing the risk of downtime and complexity.

Deploying a multi-tier web application with Amazon RDS Multi-AZ for the database and Amazon ElastiCache for caching provides a highly available, fault-tolerant, and optimized architecture. Automatic failover ensures continuous operation, while caching improves application responsiveness and reduces database load. This approach meets the business requirement for minimal downtime, high performance, and operational simplicity, making it the most suitable choice among the options provided.

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