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AWS Certified Solutions Architecture - Associate (SAA-C03)
AWS Certified Solutions Architecture - Associate (SAA-C03)

Practice Test #4

Simule a experiência real do exame com 65 questões e limite de tempo de 130 minutos. Pratique com respostas verificadas por IA e explicações detalhadas.

65Questões130Minutos720/1000Nota de Aprovação
Ver Questões de Prática

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Cada resposta é verificada por 3 modelos de IA líderes para garantir máxima precisão. Receba explicações detalhadas por alternativa e análise aprofundada das questões.

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Questões de Prática

1
Questão 1

A media streaming company operates a video processing service that uploads and downloads terabytes of video content daily to an Amazon S3 bucket located in the us-west-2 region. The processing service runs on Amazon EC2 instances deployed in private subnets across multiple Availability Zones within a VPC in the same region. Currently, all S3 communication flows through a NAT Gateway in the public subnet, resulting in significant data transfer charges of approximately $500 per month. The company needs to optimize their network architecture to minimize data egress costs while maintaining secure access from private subnets. Which solution will meet these requirements MOST cost-effectively?

A NAT instance can sometimes reduce hourly cost compared to a NAT Gateway, but it is not the most cost-effective or operationally sound solution for heavy S3 traffic. It still forces all S3 data through NAT (unnecessary), requires patching, scaling, and HA design (failover scripts, multiple instances). For terabytes/day, throughput limits and management overhead make it inferior to an S3 Gateway Endpoint.

This option is architecturally incorrect. A NAT instance must be in a public subnet with a route to an Internet Gateway to provide egress; placing it in a private subnet prevents it from reaching the internet. Also, routing “S3 traffic” from a public subnet route table to a NAT instance does not solve private subnet egress patterns. It neither reduces cost effectively nor aligns with correct NAT design.

Correct. An S3 VPC Gateway Endpoint allows private subnets to reach S3 without traversing a NAT Gateway or the public internet. You update private route tables with the S3 prefix list targetting the endpoint. This removes NAT data processing charges for S3 traffic and improves security. You can further lock down access using endpoint policies and S3 bucket policies conditioned on the endpoint ID.

Adding a second NAT Gateway increases hourly costs and does not eliminate NAT per-GB data processing charges; it may actually increase total spend. While multi-AZ NAT Gateways can improve resilience and avoid cross-AZ routing charges when configured per-AZ, the core issue here is that S3 traffic should not use NAT at all. The most cost-effective fix is an S3 Gateway Endpoint.

Análise da Questão

Core Concept: This question tests cost-optimized private connectivity from a VPC to Amazon S3. The key services are NAT Gateway (for general outbound internet access) versus an S3 VPC Gateway Endpoint (private, AWS-network routing to S3 without NAT). Why the Answer is Correct: Using a NAT Gateway for S3 access from private subnets forces S3 traffic to traverse the NAT Gateway, incurring NAT data processing charges (and potentially additional data transfer components depending on architecture). For workloads moving terabytes daily, NAT processing costs can become significant. An S3 VPC Gateway Endpoint provides a direct route from the private subnets to S3 over the AWS backbone, eliminating the need for NAT for S3 access. This is typically the MOST cost-effective approach because gateway endpoints have no hourly charge and no per-GB data processing fee like NAT Gateways. It also improves security posture by keeping traffic off the public internet. Key AWS Features: 1) VPC Gateway Endpoint for S3: Adds a prefix-list route to S3 in the private subnet route tables. 2) Endpoint policy: Restricts which S3 buckets/actions are allowed (least privilege). 3) S3 bucket policy with aws:SourceVpce condition: Ensures the bucket is only accessible via the endpoint. 4) Multi-AZ: Gateway endpoints are highly available and scale horizontally; no per-AZ deployment required. Common Misconceptions: A common trap is thinking a cheaper NAT instance will reduce costs (Option A). While it may reduce hourly NAT cost, it does not provide the same managed scalability/availability and still routes S3 traffic through NAT, which is unnecessary when an endpoint exists. Another misconception is adding more NAT Gateways (Option D) to “optimize” traffic; this increases hourly costs and does not remove per-GB processing charges. Exam Tips: When the destination is S3 (or DynamoDB) from private subnets in the same region, default to a VPC Gateway Endpoint for cost and security. Use NAT Gateway/instance primarily for general internet egress (software updates, external APIs). Look for keywords like “S3 from private subnets,” “reduce NAT data charges,” and “keep traffic private” to quickly identify gateway endpoints as the best answer.

2
Questão 2

A healthcare organization stores patient medical records and diagnostic images in Amazon S3 buckets across multiple departments. Due to strict HIPAA compliance requirements, all stored medical data must remain completely private and never be exposed to unauthorized access. The organization needs to implement a comprehensive solution that prevents any accidental public exposure of medical data across all S3 buckets in their AWS account, with approximately 150TB of sensitive medical data distributed across 50+ buckets. Which solution will most effectively meet these security requirements?

GuardDuty is primarily a detective service. While it can generate findings related to S3 protection (e.g., suspicious access patterns) and you can automate remediation with Lambda, this is still reactive. There can be a time gap between a misconfiguration that makes a bucket public and the remediation action. For "never be exposed," preventive controls like S3 Block Public Access are more appropriate.

Trusted Advisor can report publicly accessible S3 buckets, but it is not a preventive control and relies on periodic checks and manual remediation. Email notifications and human intervention introduce delay and operational risk, which conflicts with strict HIPAA requirements. This option does not scale well across many buckets and does not guarantee that data is never exposed.

AWS Resource Access Manager (RAM) is for sharing resources across AWS accounts (e.g., subnets, Transit Gateways) and is not used to discover or manage public S3 bucket access. The described detection-and-remediation flow is not aligned with how RAM works. Even if implemented with other services, it would still be reactive rather than a strong preventive guardrail.

S3 Block Public Access at the account level is the strongest preventive control available for this requirement. It applies to all 50+ buckets simultaneously — both existing and any future buckets — without requiring per-bucket configuration. It overrides any bucket-level ACL or bucket policy that would otherwise grant public access, directly satisfying the 'never be exposed' requirement. Enforcing this setting with an AWS Organizations SCP adds a governance layer that prevents any IAM user or role (including account administrators) from disabling BPA, creating a durable and tamper-resistant guardrail. This combination is the canonical approach for HIPAA-regulated environments requiring preventive — not reactive — public access controls.

Análise da Questão

Core Concept: This question tests preventive controls for Amazon S3 public exposure at scale, using S3 Block Public Access (BPA) and governance guardrails (AWS Organizations SCPs). For HIPAA-regulated data, the goal is to prevent accidental misconfiguration rather than detect-and-remediate after exposure. Why the Answer is Correct: S3 Block Public Access at the account level is the most comprehensive, scalable control to ensure no bucket (existing or future) can become public due to ACLs or bucket policies. Account-level BPA can (1) block public ACLs, (2) ignore public ACLs, (3) block public bucket policies, and (4) restrict public bucket policies. This directly addresses "never be exposed," because it prevents public access from being granted in the first place, across 50+ buckets and large data volume without needing per-bucket automation. Adding an AWS Organizations service control policy (SCP) to deny actions that would disable or modify account-level BPA (and related S3 public-access settings) enforces the control even if an IAM principal has broad permissions. This creates a durable guardrail aligned with least privilege and defense-in-depth. Key AWS Features: - S3 Block Public Access (account level): centralized, applies to all buckets in the account, and overrides bucket-level public settings. - SCPs in AWS Organizations: explicit deny that cannot be bypassed by IAM permissions, ideal for compliance guardrails. - Works regardless of data size (150 TB) because it is a control-plane setting, not a data-plane scan. Common Misconceptions: Detection services (GuardDuty, Trusted Advisor) can alert on exposure, but they do not guarantee prevention; there can be a window of public access before remediation. Also, "manual remediation" is not acceptable for strict requirements. Exam Tips: When requirements say "prevent accidental public exposure across all buckets," prefer S3 Block Public Access (account level) plus governance enforcement (SCPs). Use detective controls (GuardDuty, Config) as complementary, not primary, for "must never be public" scenarios.

3
Questão 3

A ride-hailing startup stores trip events in an Amazon DynamoDB table. Due to frequent bug rollbacks, the team must restore the table to any point in the last 24 hours without manual scheduling. Enable point-in-time recovery within the last 24 hours with the least operational effort. Which solution meets the requirements with the LEAST operational overhead?

Correct. DynamoDB Point-in-Time Recovery (PITR) provides continuous backups and lets you restore to any second within the retention window (up to 35 days), easily covering the last 24 hours. It requires only enabling the feature—no scheduling, no custom code, and minimal ongoing operations. This best matches the requirement for least operational overhead and frequent rollback recovery.

Not the best fit. AWS Backup can manage DynamoDB backups via backup plans and schedules, which reduces some effort but still typically restores to discrete backup points rather than any second in time. Meeting “any point in the last 24 hours” would require very frequent backups and still wouldn’t match PITR’s granularity. Operationally, you must manage backup plans, schedules, and retention policies.

Incorrect. Hourly on-demand backups via Lambda introduce operational overhead (Lambda code, IAM permissions, scheduling, monitoring, retries, error handling) and only provide hourly restore points, not “any point in time.” It also risks gaps if executions fail or throttling occurs. This is higher effort and lower recovery precision than DynamoDB PITR.

Incorrect for least-ops restore. DynamoDB Streams plus S3 storage can support custom replay/event sourcing, but it requires building and operating a replay mechanism, handling ordering, duplicates, schema changes, and retention. It’s complex and operationally heavy compared to PITR. Also, it’s not a native point-in-time restore feature for the table state without additional processing.

Análise da Questão

Core Concept: This question tests DynamoDB data protection and recovery, specifically Point-in-Time Recovery (PITR) for operational resilience. The requirement is to restore the table to any point within the last 24 hours without manual scheduling and with the least operational effort. Why the Answer is Correct: Enabling DynamoDB PITR provides continuous backups and allows restoring a table to any second within a rolling retention window (up to 35 days). Because the team needs restores within the last 24 hours and wants no manual scheduling, PITR is purpose-built: once enabled, DynamoDB automatically maintains the restore history. Operational overhead is minimal—no custom jobs, no backup schedules, and no additional data pipeline to manage. Key AWS Features: - DynamoDB PITR: Continuous, incremental backups with per-second granularity within the retention window. - Restore behavior: PITR restores to a new table (you then swap traffic, update configuration, or rename as needed), which is a common resilience pattern. - No scheduling/automation required: Unlike on-demand backups, PITR does not require cron, Lambda, or external orchestration. - Aligns with AWS Well-Architected Reliability pillar: supports recovery objectives and reduces human error by automating backups. Common Misconceptions: - AWS Backup can protect DynamoDB, but it typically relies on scheduled backups (or at least managed backup plans). Even if you schedule frequent backups, you won’t get true “any point in time” restore at second-level granularity; you restore to backup points. PITR is the DynamoDB-native feature explicitly designed for point-in-time restores. - Lambda-driven hourly backups sound simple, but they introduce operational burden (code, permissions, monitoring, retries, throttling, cost management) and still only provide hourly restore points. - DynamoDB Streams to S3 for replay is an event-sourcing approach, not a low-ops restore mechanism. It requires building and operating a replay system, handling ordering, idempotency, and retention. Exam Tips: When you see “restore to any point in time” for DynamoDB with minimal operations, choose PITR. If the question instead asks for periodic backups, centralized policy management across services, or compliance-driven retention, AWS Backup may be appropriate. Always distinguish “point-in-time” (continuous) from “point-in-schedule” (snapshots/backups).

4
Questão 4
(Selecione 2)

A multinational corporation uses several Amazon RDS for Oracle On-Demand DB instances with consistent high utilization for their critical business applications. These RDS DB instances are managed in different member accounts that are part of an AWS Organizations structure. The company's financial planning team, with access to all accounts, is tasked with identifying potential cost-saving opportunities by leveraging AWS cost optimization tools. Which combination of steps will the financial planning team take to most effectively identify cost optimization opportunities? (Choose two.) Which combination of steps will meet these requirements? (Choose two.)

The “Amazon RDS Idle DB Instances” Trusted Advisor check is useful when databases are underutilized or unused, helping identify candidates for stopping, deleting, or downsizing. However, the question explicitly states the RDS for Oracle instances have consistent high utilization, so they are not idle. This check would be unlikely to surface meaningful savings for this workload profile.

The “Amazon RDS Reserved Instance Optimization” Trusted Advisor check is the best match for consistently high On-Demand RDS usage. It identifies where purchasing RDS Reserved Instances could reduce costs compared to On-Demand pricing. For critical business applications with steady utilization, RIs are a classic cost optimization lever and a frequently tested exam pattern.

Using Trusted Advisor recommendations in the management account is the most effective approach for a finance team that needs organization-wide visibility. With AWS Organizations integration (and required support plan/features), the management account can provide consolidated insights across member accounts, reducing manual effort and ensuring consistent governance and reporting for cost optimization opportunities.

Reviewing Trusted Advisor in each member account can technically identify RI opportunities where the RDS instances run, but it is less effective for a centralized financial planning team. It requires switching accounts and aggregating results manually. In an AWS Organizations environment, centralized review from the management account is typically the intended best practice for cross-account visibility.

Trusted Advisor “compute optimization” checks and AWS Compute Optimizer are primarily focused on compute resources like EC2 instances, Auto Scaling groups, and Lambda functions. They are not the primary tools for identifying RDS for Oracle Reserved Instance purchase opportunities. While they are valuable cost tools, they do not directly address the RDS RI optimization requirement described.

Análise da Questão

Core Concept: This question tests AWS cost optimization for Amazon RDS (Oracle) across multiple AWS Organizations accounts, using AWS Trusted Advisor. The key ideas are (1) identifying savings for consistently utilized On-Demand databases via Reserved Instances (RIs) and (2) understanding how to view organization-wide recommendations when accounts are centrally managed. Why the Answer is Correct: Because the RDS for Oracle instances have consistent high utilization and are On-Demand, the most likely cost-saving opportunity is purchasing RDS Reserved Instances (or moving to other commitment constructs where applicable). Trusted Advisor’s “Amazon RDS Reserved Instance Optimization” check highlights where On-Demand usage patterns indicate potential RI savings. That directly matches the scenario. Additionally, the financial planning team has access to all accounts and needs to evaluate instances spread across member accounts. The most effective way is to use Trusted Advisor recommendations in the management account (with AWS Organizations integration), which can provide a consolidated, multi-account view rather than requiring manual per-account review. Key AWS Features: Trusted Advisor provides cost optimization checks, including RDS RI optimization. With AWS Organizations, Trusted Advisor can be used to aggregate findings across accounts (assuming the appropriate support plan and organizational view configuration). This aligns with the Cost Optimization pillar of the AWS Well-Architected Framework: commit to capacity when workloads are steady, and centralize visibility for governance. Common Misconceptions: Option A (Idle DB Instances) is a common cost check, but it does not fit “consistent high utilization.” Option D (member accounts) can work operationally, but it is less effective than centralized review when the team already has org-wide access and needs consolidated reporting. Option E is misleading because Compute Optimizer focuses on EC2, Auto Scaling, Lambda, and some container recommendations; it is not the primary tool for RDS Oracle RI purchase guidance. Exam Tips: When you see “steady/high utilization + On-Demand,” think “Reserved Instances/Savings Plans” (for RDS, typically RIs). When you see “multiple accounts under AWS Organizations + central finance team,” think “management account consolidated visibility” for cost governance tools (Cost Explorer, CUR, and where applicable, Trusted Advisor organizational view).

5
Questão 5

A healthcare organization uses Amazon S3 to store patient medical records and research documents. Due to HIPAA compliance requirements, all stored data must be properly classified and any Protected Health Information (PHI) must be identified and secured appropriately. The organization discovered that approximately 15% of their 500,000 S3 objects may contain PHI data that wasn't properly tagged during upload. They need an automated solution to continuously scan for PHI in their S3 buckets and immediately alert the compliance team when sensitive data is detected. Which solution will meet these requirements most effectively?

Correct. Amazon Macie is designed to discover and classify sensitive data in S3 (including PII/PHI-related patterns) and generates findings. Those findings can be routed through Amazon EventBridge using an event pattern that matches SensitiveData findings, then delivered to Amazon SNS for immediate human notifications (email/SMS/HTTP). This directly satisfies continuous scanning plus immediate alerting for compliance response.

Incorrect. Amazon Inspector focuses on vulnerability management for compute (EC2 instances), container images in ECR, and Lambda functions. It does not scan S3 object contents to detect PHI/PII. Even though EventBridge + SNS is a valid alerting pattern, the underlying detection service is wrong for the requirement of identifying sensitive data inside S3 objects.

Incorrect. Macie is the right detection service, but sending findings to SQS is not the most effective way to “immediately alert the compliance team.” SQS is a queue for application processing and requires a consumer to poll and then notify humans. Also, filtering specifically on SensitiveData:S3Object/Personal may be too narrow for PHI use cases; SensitiveData findings broadly are more appropriate.

Incorrect. This option is doubly mismatched: Inspector does not detect PHI in S3 objects, and SQS is not a direct alerting mechanism for a compliance team. While you could build a workflow where a consumer reads SQS and triggers notifications, it adds unnecessary components and still fails the core requirement of scanning S3 content for sensitive data.

Análise da Questão

Core Concept: This question tests data discovery and classification for sensitive data in Amazon S3, and event-driven alerting. The AWS service purpose-built for discovering and protecting sensitive data (including PHI/PII patterns) in S3 is Amazon Macie. Inspector is for vulnerability management (EC2, ECR, Lambda), not S3 content inspection. Why the Answer is Correct: Amazon Macie continuously evaluates S3 objects using machine learning and managed data identifiers to detect sensitive data such as personal information that can map to PHI-related elements. When Macie generates findings, those findings are emitted as events that can be routed via Amazon EventBridge. Creating an EventBridge rule to match Macie findings of the SensitiveData type and sending an Amazon SNS notification provides immediate, push-based alerting to the compliance team (email, SMS, HTTPS endpoints), meeting the “immediately alert” requirement. Key AWS Features / Best Practices: Macie supports scheduled and continuous discovery jobs for S3 buckets, enabling ongoing scanning as new objects arrive or existing objects change. Findings include severity, affected bucket/object, and the type of sensitive data detected. EventBridge integrates natively with Macie findings, allowing fine-grained event pattern filtering. SNS is the standard service for human-facing notifications and fanout to multiple subscribers. For HIPAA-aligned security posture, combine this with S3 default encryption (SSE-KMS), least-privilege IAM, and optionally automated remediation (e.g., Lambda to apply tags/quarantine prefixes) triggered from EventBridge. Common Misconceptions: A frequent trap is choosing Amazon Inspector because it is also a “security scanning” service. However, Inspector does not inspect S3 object contents for PHI/PII; it identifies software vulnerabilities and unintended network exposure. Another trap is using SQS for “alerting.” SQS is for decoupling and buffering between systems, not direct notification to people. Exam Tips: When you see “discover/classify sensitive data in S3” or “PII/PHI detection,” think Amazon Macie. When you see “CVEs, package vulnerabilities, EC2/ECR/Lambda scanning,” think Amazon Inspector. For immediate notifications to teams, SNS is typically the best fit; SQS is better for downstream processing workflows. References (AWS docs): Amazon Macie findings and EventBridge integration; SNS notifications; Inspector supported resources and use cases.

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6
Questão 6

A healthcare organization operates a patient portal where medical professionals upload diagnostic images and patient documentation. The organization needs to implement automated content filtering to prevent inappropriate or non-medical images from being uploaded to maintain compliance with healthcare regulations. The solution must automatically detect and block inappropriate content in uploaded images without requiring custom machine learning model development or training. The filtering must occur in real-time during the upload process with minimal latency impact on the web application. Which solution will meet these requirements?

Amazon SageMaker JumpStart can help deploy prebuilt or foundation-model-based solutions, but it still requires selecting, deploying, and operating an inference endpoint. That introduces more complexity and operational overhead than necessary for a requirement already covered by a native managed moderation API. The question explicitly says no custom model development or training is required, which strongly favors Rekognition over SageMaker. While technically possible, this is not the most direct, lowest-latency, or most AWS-native solution for image moderation.

This option uses Amazon Rekognition image moderation to detect inappropriate content without requiring any custom model development or training. AWS Lambda provides a lightweight serverless integration layer so the patient portal can invoke moderation logic during the upload workflow and make an immediate accept-or-reject decision. Rekognition’s DetectModerationLabels API is purpose-built for identifying explicit or unsafe visual content in still images, which directly matches the requirement. This approach is operationally simple, scales automatically, and avoids the complexity of deploying and maintaining custom inference endpoints.

Amazon CloudFront Functions are extremely lightweight edge functions intended for simple HTTP request and response manipulation, not for calling AI services or performing heavy processing. They cannot directly invoke Amazon Textract, and Textract itself is designed for OCR, form extraction, and document text analysis rather than inappropriate image detection. Even if text were extracted from an image, that would not determine whether the image content itself is inappropriate or non-medical. This option is incorrect both because of service mismatch and because the execution environment is unsuitable.

Amazon Rekognition Video is intended for analyzing video files or streams, not still images uploaded through a web portal. The question specifically concerns uploaded images, so the correct Rekognition feature would be image moderation rather than video analysis. In addition, an S3-triggered Lambda runs after the object has already been uploaded, which does not inherently provide real-time blocking during the upload transaction. That makes this option a poor fit for both the media type and the timing requirement.

Análise da Questão

Core Concept: This question tests using managed AI services for real-time content moderation without building or training custom ML models. Amazon Rekognition provides a built-in DetectModerationLabels API for identifying explicit or inappropriate visual content, which is commonly used for upload-time filtering. Why the Answer is Correct: Option B uses AWS Lambda to call Amazon Rekognition moderation APIs during the upload workflow, enabling the application to accept/reject an image in near real time. This meets the requirements: no custom model development/training, automated detection and blocking, and minimal latency impact when implemented synchronously (e.g., pre-signed upload + post-upload validation gate, or direct upload to an intake bucket with immediate validation before making the object available). Key AWS Features: - Amazon Rekognition DetectModerationLabels: managed moderation model for images; returns labels and confidence scores to drive allow/deny decisions. - AWS Lambda: serverless execution for validation logic; scales automatically with upload volume. - API Gateway: provides a low-latency HTTPS endpoint for the portal to invoke validation. - Best practice pattern: upload to a quarantine/intake location (or use pre-signed URLs), run moderation, then either move/tag the object as approved (copy to an approved bucket/prefix, add object tags/metadata) or delete/quarantine and block downstream access. This aligns with least privilege and auditability (important in healthcare). Common Misconceptions: - “Any ML service works”: Textract is for OCR, not moderation. Rekognition Video is for video streams/files, not still images. SageMaker JumpStart still implies deploying/operating an endpoint and selecting/tuning a model—more complexity than required. Exam Tips: When you see “detect inappropriate content” + “no custom training,” think Amazon Rekognition moderation labels. For “real-time during upload,” choose synchronous invocation patterns (API Gateway/Lambda) or an intake bucket with immediate gating before content becomes accessible. Also note service fit: Rekognition (images), Rekognition Video (videos), Textract (text extraction), SageMaker (custom/managed ML deployment).

7
Questão 7

A financial services company operates a real-time trading platform that processes stock market data. The trading data is stored in an Amazon RDS PostgreSQL DB instance with 8 vCPUs and 32 GB RAM. During peak trading hours (9 AM - 4 PM), the system experiences heavy read operations for market analysis while simultaneously handling trade executions. The database team has identified that 75% of database operations are read queries for generating trading reports and market analytics. The current single database instance is experiencing CPU utilization of 85% during peak hours, causing query response times to increase from 50ms to 300ms. What should the solutions architect recommend to improve database performance and reduce query response times with minimal infrastructure changes?

Incorrect. Multi-AZ is primarily for high availability and automatic failover, not for scaling reads. In standard RDS Multi-AZ, the standby cannot accept traffic, so you cannot use it for writes or reads. Directing reads to the primary and writes to the standby is not supported and would not address the read-driven CPU bottleneck causing increased latency.

Incorrect. This option assumes the standby in a Multi-AZ deployment can serve read traffic. In standard Amazon RDS Multi-AZ, the standby is not readable; it is maintained for failover only. Therefore, you cannot offload analytical reads to the standby, and the primary would remain CPU-constrained during peak trading hours.

Read replicas are the correct mechanism for offloading read-heavy workloads from an RDS primary instance. Routing the 75% analytical/reporting queries to read replicas directly relieves CPU pressure on the primary (currently at 85%), allowing it to focus on write operations (trade executions). Starting with smaller replicas (4 vCPU/16 GB) satisfies the "minimal infrastructure changes" requirement — replica sizing can be independently scaled up or additional replicas added if ReplicaLag or CPU metrics indicate the need. This is the canonical AWS pattern for read/write splitting on RDS and aligns with the Well-Architected Performance Efficiency pillar.

Partially reasonable but not the best choice given "minimal infrastructure changes." Read replicas are correct, but matching the primary size (8 vCPU/32 GB) may be unnecessary and increases cost without evidence that smaller replicas won't meet the analytics workload. A better approach is to start with smaller replicas, monitor performance/lag, and scale up/out only if required.

Análise da Questão

Core Concept: This question tests Amazon RDS PostgreSQL scaling patterns for read-heavy workloads. The key concept is separating read scaling (horizontal) from write scaling by using RDS Read Replicas, rather than relying on Multi-AZ (which is primarily for high availability). Why the Answer is Correct: The workload is 75% reads (analytics/reports) and the primary instance is CPU-bound (85%) during peak hours, increasing latency. Creating read replicas and routing analytical/read-only queries to them offloads CPU and I/O from the primary instance, allowing the primary to focus on write transactions (trade executions) and critical reads. This directly targets the bottleneck with minimal infrastructure change: add replicas and update the application's connection routing (or use a reader endpoint/proxy pattern). Key AWS Features: RDS Read Replicas for PostgreSQL use asynchronous replication from the primary. They are designed to scale read throughput and reduce read latency on the writer by distributing read traffic. You can create one or more replicas and size them independently. For minimal change and cost efficiency, starting with smaller replicas (4 vCPU/16 GB) is reasonable; you can monitor replica CPU/lag and scale up or add more replicas as needed. This aligns with AWS Well-Architected Performance Efficiency: use managed services and scale-out for read-heavy patterns. Common Misconceptions: Multi-AZ is often mistaken as a read-scaling feature. In standard RDS Multi-AZ, the standby is not readable and exists for failover; you cannot direct reads to it. Even in Multi-AZ DB cluster variants, the question's options describe classic "standby instance" behavior incorrectly for read routing. Therefore, Multi-AZ won't reduce peak read CPU on the primary in the way required. Exam Tips: When you see "mostly reads" + "high CPU/latency" on RDS, think Read Replicas and read/write splitting. When you see "availability/failover/RTO/RPO," think Multi-AZ. Also note that replica sizing is flexible; choose the smallest change that meets performance goals and can be iterated (scale up/out) based on metrics like CPUUtilization, ReadIOPS, and ReplicaLag.

8
Questão 8

A financial technology startup is building a secure trading platform on AWS. The architecture requires a VPC with both public and private subnets across three Availability Zones for regulatory compliance and fault tolerance. The public subnets will host load balancers and bastion hosts, while the private subnets will contain the core trading application servers and database instances. The trading application servers in private subnets need internet connectivity to download critical security patches, connect to external market data feeds, and communicate with third-party payment processors. However, these servers must not be directly accessible from the internet for security reasons. What should the solutions architect implement to provide secure outbound internet access for the trading application servers in private subnets while maintaining the highest level of availability?

Correct. Deploying a NAT gateway in each public subnet (one per AZ) and routing each private subnet to its local NAT gateway is the AWS best practice for high availability and fault isolation. NAT gateways are managed and scale automatically within an AZ. AZ-local routing avoids cross-AZ egress, reduces latency, and ensures other AZs retain outbound connectivity if one AZ fails.

Incorrect. NAT instances can provide outbound access, but they are self-managed EC2 instances requiring patching, scaling, and careful configuration (source/destination check disabled, security groups, failover scripts). Placing them in private subnets is also wrong because a NAT device must be able to reach the internet via an IGW, which typically requires being in a public subnet with a public IP/EIP.

Incorrect. You cannot attach an internet gateway to a subnet; an IGW attaches to a VPC. Also, routing private subnets directly to an IGW would make them effectively public (if instances have public IPs) and violates the requirement that trading application servers must not be directly accessible from the internet. A “secondary IGW” is not a valid construct for this purpose.

Incorrect. An egress-only internet gateway is used for IPv6 to allow outbound-only internet access while blocking inbound-initiated connections. It does not provide IPv4 NAT functionality, which is what most patch repositories, market data feeds, and third-party endpoints commonly use. Also, egress-only IGWs are attached to the VPC (not placed in a subnet) and do not replace NAT gateways for IPv4.

Análise da Questão

Core Concept: This question tests secure outbound internet access from private subnets using Network Address Translation (NAT) while maintaining high availability across multiple Availability Zones (AZs). In AWS, private subnets cannot have a route to an Internet Gateway (IGW) and still be “private”; instead, they use a NAT device in a public subnet to initiate outbound connections while preventing unsolicited inbound internet access. Why the Answer is Correct: Option A (NAT Gateways in each AZ with AZ-local routing) provides the highest availability and best practice design. A NAT gateway is an AWS-managed, highly available service within an AZ. By deploying one NAT gateway per AZ and routing each private subnet’s 0.0.0.0/0 traffic to the NAT gateway in the same AZ, you avoid cross-AZ dependencies. If an AZ experiences an impairment, private subnets in other AZs still have outbound access through their local NAT gateway, meeting fault-tolerance and regulatory expectations. Key AWS Features / Configurations: - Place NAT gateways in public subnets that have a default route (0.0.0.0/0) to the IGW. - Private subnet route tables should send 0.0.0.0/0 to the NAT gateway (not to the IGW). - Use one route table per private subnet (or per AZ) to ensure AZ affinity. - NAT gateways scale automatically and are managed, reducing operational risk compared to self-managed NAT instances. - Security posture: instances remain without public IPs; inbound from the internet is not possible because NAT is for outbound-initiated flows. Common Misconceptions: - “NAT instances are equivalent”: they require patching, scaling, failover automation, and can become bottlenecks—undesirable for a trading platform. - “Attach an IGW to private subnets”: IGWs attach to VPCs, not subnets, and routing private subnets to an IGW makes them public if they have public IPs. - “Egress-only IGW solves it”: egress-only IGWs are for IPv6 outbound-only traffic and do not replace NAT for IPv4. Exam Tips: For multi-AZ private subnet outbound internet, the exam-preferred pattern is “NAT gateway per AZ + private subnet routes to same-AZ NAT.” Remember: IGW is for public subnets; NAT is for private subnet egress (IPv4). Egress-only IGW is specifically for IPv6.

9
Questão 9

A startup company has developed a video thumbnail generation platform where content creators can upload videos and automatically generate custom thumbnails with various templates and effects. Users upload video files along with configuration data specifying thumbnail styles, timestamps, and overlay effects they want to apply. The platform currently runs on a single Amazon EC2 instance and uses Amazon DynamoDB to store configuration metadata. As the platform gains popularity with streamers and YouTubers, user traffic is experiencing significant fluctuations - peak usage occurs during evening hours (8-11 PM) and weekends when content creators are most active. The company needs to ensure the platform can automatically scale to handle varying workloads from 50 concurrent users during off-peak to 2000+ concurrent users during peak times. Which solution best meets the scalability requirements for this video thumbnail generation platform?

Lambda is a good compute choice for bursty workloads, but DynamoDB is not appropriate for storing video files. DynamoDB has a 400 KB item size limit and is optimized for key-value/document metadata, not large binaries. Even if videos were chunked, costs and complexity would be high and performance unpredictable. This option fails the storage requirement for large uploaded media.

Kinesis Data Firehose is designed to ingest and deliver streaming data to targets like S3, Redshift, or OpenSearch with optional lightweight transformations. It is not a general-purpose video processing service and does not run arbitrary thumbnail generation workloads. It also is not intended to store configuration metadata as a primary database. This option mismatches the workload type.

S3 is the correct service for storing uploaded video objects at scale and handling traffic spikes without provisioning. Lambda can automatically scale out to process uploads (often triggered by S3 events) and generate thumbnails. DynamoDB remains ideal for configuration metadata with on-demand/auto scaling capacity. This combination provides elasticity, durability, and minimal operations for highly variable demand.

A fixed increase to five EC2 instances does not provide automatic scaling from 50 to 2000+ concurrent users; it is unlikely to meet peak demand and wastes capacity off-peak. EBS volumes are attached storage and not a scalable shared repository for uploads across instances without additional architecture. This approach increases operational burden and reduces resilience compared to managed/serverless options.

Análise da Questão

Core Concept: This question tests designing a resilient, automatically scaling, event-driven architecture for spiky workloads using managed services. The key pattern is decoupling ingestion (object storage) from processing (serverless compute) while keeping metadata in a low-latency database. Why the Answer is Correct: Option C (Lambda + S3 for videos + DynamoDB for configuration metadata) best matches the requirement to scale from ~50 to 2000+ concurrent users with large fluctuations. Amazon S3 provides virtually unlimited, highly durable storage for uploaded video objects and naturally absorbs traffic spikes without pre-provisioning. AWS Lambda can scale horizontally by invoking functions per event (for example, S3 ObjectCreated events, or via an API layer that writes to S3 and then triggers processing). DynamoDB remains a strong fit for configuration metadata due to its low-latency access and ability to scale throughput (on-demand or auto scaling) with demand. Key AWS Features: - Amazon S3: durable object storage (11 9s durability), high request rates, event notifications to trigger processing. - AWS Lambda: automatic scaling, pay-per-use, concurrency controls (reserved concurrency) to protect downstream systems. - DynamoDB: on-demand capacity or auto scaling, TTL for ephemeral job/config records, Streams if needed for additional eventing. - (Common real-world enhancement) Add SQS between S3 and Lambda to buffer bursts and control concurrency, though not required by the option. Common Misconceptions: Option A looks attractive because Lambda scales, but storing large video binaries in DynamoDB is a poor fit: DynamoDB has item size limits (400 KB) and is not intended for large object storage. Option B misuses Kinesis Data Firehose, which is for streaming data delivery to destinations (S3, OpenSearch, Redshift) and not for general video processing workflows. Option D is classic “scale up/scale out EC2,” but a fixed fleet of five instances will not reliably handle 2000+ concurrent users, and EBS is not a scalable shared upload store; it also increases operational overhead and reduces elasticity. Exam Tips: For media/file upload workloads with spiky demand, default to S3 for objects, DynamoDB for metadata, and Lambda (or container services) for processing. Watch for DynamoDB size limits and recognize Firehose as delivery/ETL for streaming records, not a compute engine. When you see “automatic scaling with large fluctuations,” serverless + managed storage is usually the intended answer.

10
Questão 10

A financial services company operates its trading platform on AWS infrastructure. The company needs to establish a secure connection to access real-time market data from a third-party financial data provider. The data provider hosts their market data service in their own dedicated VPC on AWS. According to the company's compliance team, the connection must remain completely private and be restricted only to the specific market data service. Additionally, all connections must be initiated exclusively from the financial company's VPC for audit trail purposes. Which solution will meet these security and connectivity requirements?

VPC peering provides private IP connectivity between two VPCs, but it is network-level access. Once routes and security rules allow it, the consumer can potentially reach many resources in the provider VPC, not just a single market data service. This fails the requirement to restrict connectivity only to the specific service and is often disallowed in regulated environments due to broader blast radius.

A virtual private gateway (VGW) is used for Site-to-Site VPN or Direct Connect, not for AWS PrivateLink. PrivateLink does not require a VGW on the provider side; it requires a VPC endpoint service backed by an NLB. This option reflects a common confusion between private connectivity mechanisms (VPN/DX) and service-level private access (PrivateLink).

A NAT gateway enables instances in private subnets to initiate outbound connections to the internet via a public IP. It does not create private connectivity to a third-party VPC service and would typically route traffic over the public internet (even if the destination is in AWS). This violates the “completely private” requirement and does not restrict access to only the specific service.

This is the correct approach: the provider exposes the market data application as a PrivateLink endpoint service, and the financial company creates an interface VPC endpoint to consume it. Connectivity is private, stays on the AWS network, and is scoped to that specific service rather than the entire provider VPC. All sessions are initiated from the consumer VPC to the endpoint ENIs, supporting audit requirements.

Análise da Questão

Core Concept: This question tests AWS PrivateLink for private, service-specific connectivity between VPCs, especially when consuming a third-party service hosted in another AWS account or VPC. Why the Answer is Correct: The requirements are fully private connectivity, access restricted only to the specific market data service, and connections initiated only from the financial company’s VPC. AWS PrivateLink is designed for exactly this pattern: the service provider publishes the application as a VPC endpoint service, and the consumer creates an interface VPC endpoint in its own VPC. This avoids exposing the entire provider VPC and keeps all traffic on the AWS backbone rather than traversing the public internet. Although option D says the provider should create a “VPC endpoint,” the intended and technically correct model is that the provider creates a VPC endpoint service and the consumer creates the VPC endpoint. Key AWS Features / Configurations: - Provider side: create a VPC endpoint service backed by a Network Load Balancer and optionally require acceptance for consumer connections. - Consumer side: create an interface VPC endpoint in the company VPC and attach security groups to control access. - DNS: private DNS can be used so the service resolves to private IP addresses within the consumer VPC. - Compliance and audit: traffic stays within AWS private networking, and CloudTrail plus VPC Flow Logs can help support auditing and monitoring. Common Misconceptions: VPC peering is private but too broad because it enables network-level connectivity between VPCs rather than limiting access to a single service. NAT gateways are for internet egress, not private service consumption across VPCs. Virtual private gateways are used for VPN or Direct Connect, not for implementing PrivateLink. Exam Tips: When a question mentions a third-party service in another VPC, requires private connectivity, and wants access limited to only one application or service, AWS PrivateLink is usually the best answer. Prefer PrivateLink over VPC peering when you need service-level isolation and consumer-initiated access. Be careful with terminology: the provider creates the endpoint service, and the consumer creates the interface endpoint.

Histórias de Sucesso(31)

이
이**Apr 25, 2026

Período de estudo: 1 month

시험문제보다 난이도가 있는편이고 같은문제도 조금 나왔습니다

C
C*********Mar 23, 2026

Período de estudo: 1 week

요구사항 정확히 인지하기(이거 젤중요 이 훈련이 제일 중요한듯), 오답노트 갈겨서 200문제만 확실히 해서 갔어요 실제 시험 지문은 훨씬 간단한데 난이도는 앱이랑 비슷하거나 더 낮았던것같아요 느낌상 탈락이었는데 통과해서 기쁘네요 큰 도움 되었습니다 감사해요!

소
소**Feb 22, 2026

Período de estudo: 1 week

그냥 문제 풀면서 개념들 GPT에 물어보면서 학습했어요 768점 턱걸이 합격,,

조
조**Jan 12, 2026

Período de estudo: 3 months

그냥 꾸준히 공부하고 문제 풀고 합격했어요 saa 준비생분들 화이팅!!

김
김**Dec 9, 2025

Período de estudo: 1 month

앱으로는 4일만에 몇 문제를 풀었는지 모르겠지만 1딜동안 aws 기본 개념부터 덤프로 시나리오 그려보고 하니까 합격했습니다. 시험은 생각보다 헷갈리게 나와서 당황했는데 30분 추가 테크로 flag한 지문들 다시 확인하니까 문제 없었습니다.

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