Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in Amazon S3 without the need for a data warehouse. It is cost-effective because you only pay for the queries you run, and it can handle Apache Parquet files efficiently. Additionally, Athena integrates with KMS, making it suitable for querying encrypted data.
Key AWS features:
* Cost-Effective: Athena charges only for the data scanned by the queries, making it a more cost- effective solution compared to Redshift or EMR for occasional queries.
* Direct S3 Querying: Athena supports querying data directly in S3, including Parquet files, without needing to move the data.
* AWS Documentation: Athena's compatibility with encrypted Parquet files in S3 makes it the ideal choice for this scenario, reducing both cost and complexity.

Why Option D is Correct:
GPU Access: Only EC2 instances in the GPU family (e.g., P2, P3) can provide GPU resources.
ECS Orchestration: Simplifies container deployment and management.
Why Other Options Are Not Ideal:
Option A: Lambda does not support GPU-based runtimes.
Option B: AWS Fargate does not support GPU-based workloads.
Option C: ECR is a container registry, not an orchestration or execution service.
AWS References:
Amazon ECS with GPU Instances:AWS Documentation - ECS GPU Instances

AWS Direct Connect provides a dedicated network connection from on-premises to AWS, offering greater bandwidth and more consistent performance than internet-based connections or VPN. AWS PrivateLink enables secure, private connectivity to supported AWS services such as Kinesis Data Firehose over Direct Connect, bypassing the public internet and providing the highest throughput and lowest latency possible. This is the recommended solution for consistently transferring large volumes of data with maximum reliability and performance.
Reference Extract from AWS Documentation / Study Guide:
"AWS Direct Connect and AWS PrivateLink provide private, high-throughput connectivity between on- premises and AWS services, bypassing the public internet and ensuring maximum performance for large data transfers." Source: AWS Certified Solutions Architect - Official Study Guide, Hybrid Networking section.

ElastiCache for Redis OSS supportsMulti-AZ with automatic failover, which is exactly the feature required here. AWS documentation states that when Multi-AZ is enabled and the primary node fails, ElastiCache automatically promotes a read replica to primary, minimizing downtime and restoring write capability without requiring manual promotion steps. Backups are useful for recovery, but they do not provide automatic operational failover. A design that depends on manual promotion is also weaker than native automatic failover. Since the question explicitly asks for a solution that canautomatically fail over, the correct answer is to enableMulti-AZfor the replication group.

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.