Get Amazon SAA-C03 Exam Practice Questions - Real and Updated

Answer : D

At 10 Mbps, the maximum transferable data in 30 days is far below 200 TB, making any online transfer (Options A, B, C) impossible within the time window.

AWS Snowball Edge storage-optimized devices support high-speed, offline bulk data migration of large datasets. Once the data is delivered to S3, FSx for Lustre can be linked to the S3 bucket to populate the Lustre filesystem.

DataSync cannot meet the time constraint over 10 Mbps. Storage Gateway is not designed for large-scale migrations.

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Answer : D

For short, frequent daily spikes (15--20 minutes), the most cost-effective scaling behavior typically comes from target tracking scaling driven by a request-based metric, because it continuously adjusts capacity to maintain a chosen target value rather than adding/removing capacity in coarse steps. Option D uses a custom CloudWatch metric representing the number of GET requests (sourced from ALB access logs, application instrumentation, or request counting logic) and applies target tracking so the Auto Scaling group scales out and in proportionally to demand. This helps avoid both under-provisioning (bad performance during spikes) and over-provisioning (wasted cost between spikes), which is exactly what cost-optimized elasticity aims for.

Option A (simple scaling) is usually less cost-efficient for bursty traffic because it relies on fixed adjustments and alarm thresholds, often with cooldowns. With 15--20 minute spikes, simple scaling can react too slowly, overshoot, or oscillate, leaving extra instances running after the spike or failing to ramp fast enough at the start. Option B (predictive scaling) is intended for regular, forecastable demand patterns, but it's not always the most cost-effective choice for repeated short spikes because it can pre-scale conservatively and may require more historical data and tuning; it is often paired with dynamic scaling anyway. Option C uses UnhealthyHostCount, which is a health signal---not a demand signal---and scaling on unhealthy hosts can increase cost without addressing request volume; it may also mask underlying application issues rather than scaling to meet load.

Because the requirement explicitly allows standard or custom metrics and focuses on scaling based on request volume, D best matches a cost-efficient, responsive approach that directly tracks incoming workload.

Answer : C

Amazon S3 Access Grantsis designed to providefine-grained, prefix-level accessto S3 data for users and groups, including identities coming from a corporate directory or IAM Identity Center. AWS documentation explains that S3 Access Grants can map users or groups to specific S3 locations and vend temporary credentials, which makes it far more scalable than building separate IAM users, role-sharing workflows, or presigned URLs for every vendor. IAM Identity Center adds centralized user lifecycle and identity administration. This combination is especially strong when different vendors need access todifferent datasetsin the same data lake. It is also more maintainable and secure than issuing permanent IAM credentials. For auditing, the environment can still rely on AWS logging controls while using Access Grants for access distribution.

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Answer : C

Predictive scaling in EC2 Auto Scaling uses machine learning to analyze historical load and forecast future capacity needs, then pre-scales capacity before the demand occurs. It is specifically designed for recurring, cyclical workloads such as weekly batch jobs.

In this scenario:

The workload pattern is known and recurring (weekly scripted jobs).

The company explicitly does not want to manually analyze trends.

They need capacity to be ready 30 minutes before jobs start.

Predictive scaling lets you:

Set the metric (CPU utilization) and target (60%).

Configure the policy to pre-launch instances 30 minutes in advance, satisfying the requirement automatically with minimal operational overhead.

Dynamic scaling (Option A) reacts after CPU increases, not before.

Scheduled scaling (Option B) requires manual analysis and ongoing adjustment of capacity values.

EventBridge + Lambda (Option D) introduces unnecessary custom logic and still reacts after CPU reaches 60%, not 30 minutes before.

Answer : C

To meet the requirements, the key must support automatic rotation, and the company must be able to disable (deactivate) the key and schedule deletion. In AWS KMS, these lifecycle controls are available for customer managed keys. Automatic key rotation is supported for symmetric customer managed KMS keys used for encryption and decryption operations. When automatic rotation is enabled, AWS KMS generates new cryptographic material for the key on a regular schedule while the key ID remains the same, helping organizations meet compliance and security best practices without manual operational work.

Option C is the only choice that explicitly combines a symmetric customer managed key with automatic rotation enabled, which directly satisfies the rotation requirement. As a customer managed key, it can also be disabled (to prevent use) and scheduled for deletion (with a waiting period), meeting the rest of the lifecycle needs.

Option A is incorrect because automatic rotation is not generally available for asymmetric KMS keys in the same way; asymmetric keys are used for specialized use cases such as signing or asymmetric encryption, and rotation behavior differs. Options B and D mention ''disabling envelope encryption,'' which is not a configurable ''option'' you turn off in KMS; envelope encryption is a recommended pattern where KMS protects data keys, and services commonly use it under the hood. Those options therefore do not describe a valid configuration that meets the requirement.

Therefore, the correct solution is to create a symmetric customer managed KMS key and enable automatic rotation, while using standard KMS controls to disable and schedule deletion when required.