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Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q188-Q193):

NEW QUESTION # 188
A company uses an Amazon Redshift cluster to ingest data from various sources. The data is shared with other internal applications for analysis and reporting.
The cluster has eight ra3.4xlarge nodes. Data ingestion runs daily from midnight to 8 AM and takes 3 hours.
The cluster has 85% average CPU utilization during ingestion. The cluster uses on-demand node pricing and is paused outside of the 8-hour daily ingestion window. Snapshots are enabled on the cluster.
The company wants to optimize this workload to reduce costs.
Which solution will meet these requirements?

A. Configure Redshift Spectrum on the existing Redshift cluster. Set up IAM permissions to allow Redshift Spectrum to access Amazon S3. Unload data from the existing cluster to an S3 bucket. Update the internal applications to query the S3 data.
B. Create a new Redshift Serverless endpoint with 64 Redshift Processing Units (RPUs) by using the most recent snapshot from the existing Redshift cluster. Update the internal applications to retrieve data from the new Redshift Serverless endpoint. Delete the existing Redshift cluster.
C. Create a new Redshift cluster with six ra3.16xlarge nodes by using the most recent snapshot from the existing cluster. Enable auto scaling. Modify the internal applications to retrieve data from the new Redshift cluster. Shut down the existing Redshift cluster.
D. Create a new Redshift cluster with eight ra3.4xlarge nodes in concurrency scaling mode by using the most recent snapshot from the existing cluster. Modify the internal applications to retrieve data from the new Redshift cluster. Shut down the existing Redshift cluster. Purchase eight 1-year All Upfront Redshift reserved nodes.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The current Redshift configuration uses eight ra3.4xlarge nodes with on-demand pricing and is paused outside an 8-hour daily window. The ingestion workload runs only 3 hours per day and has about 85% CPU utilization during ingestion, which indicates that the existing node size and count are adequate for performance. The main opportunity is cost optimization for a workload that is time-bound and does not require a continuously running provisioned cluster.
Amazon Redshift Serverless is designed for such intermittent or variable workloads. With Redshift Serverless, you configure compute capacity in terms of Redshift Processing Units (RPUs) and pay per use based on the RPU-hours consumed when queries and data ingestion jobs are actually running. When there is no activity, there is no compute charge. Redshift Serverless can also be created from an existing provisioned Redshift snapshot, allowing an easy migration path from a node-based cluster to a serverless endpoint without re-ingesting or manually moving data.
Option C creates a new Redshift Serverless endpoint with 64 RPUs from the most recent snapshot. This takes advantage of snapshots that already exist on the current cluster. The internal applications are then updated to read from the new serverless endpoint, and the existing cluster is deleted to stop incurring node-based charges. For a workload that runs only a few hours per day, moving to serverless compute is usually more cost-effective than maintaining a provisioned cluster, even if that cluster is paused outside the ingestion window, because serverless eliminates the need to manage cluster sizing and pausing and optimizes billing around actual usage.
Option A proposes staying on provisioned RA3 nodes and additionally purchasing 1-year Reserved Nodes.
Reserved Nodes apply discount to node hours whether or not the cluster is paused or fully utilized, and they are designed to be cost-effective for steady, predictable, long-running workloads. For an 8-hour-per-day workload, especially with only 3 hours of intensive ingestion, Reserved Nodes are likely to be less cost- optimal than an on-demand, usage-based serverless model. Also, concurrency scaling is primarily for handling bursty query concurrency, not for cost reduction of a regular ingestion workload.
Option B replaces the existing eight ra3.4xlarge nodes with six ra3.16xlarge nodes and enables auto scaling.
ra3.16xlarge provides significantly more capacity per node and is generally more expensive. The original workload already runs at acceptable utilization (about 85% during ingestion), so scaling up to larger nodes is not necessary for performance. Adding auto scaling on top of this likely increases costs and complexity without a clear benefit for a predictable, time-bound daily ingestion pattern.
Option D uses Redshift Spectrum and unloads data from the cluster to Amazon S3 for querying. Redshift Spectrum allows querying data stored in S3 using external tables from within a Redshift cluster, and is charged per amount of data scanned. However, the option still depends on maintaining a Redshift cluster and does not fundamentally optimize the compute cost of the ingestion workload. It also changes query patterns and data access architecture for the internal applications and may increase query cost if large volumes of S3 data are scanned by Spectrum.
Therefore, using Redshift Serverless created from the existing snapshot and paying only for compute usage during ingestion and queries, as described in option C, is the best way to optimize cost for this specific workload pattern.
References:AWS documentation for Amazon Redshift Serverless concepts such as RPU-based pricing, using snapshots from provisioned clusters to create serverless workgroups, and cost optimization guidance for intermittent and spiky analytics workloads.AWS guidance on reserved node usage and suitability for steady- state provisioned Redshift clusters.AWS descriptions of Amazon Redshift Spectrum and its pricing and use cases for querying data directly in Amazon S3.

NEW QUESTION # 189
A company is running multiple workloads in the AWS Cloud. The company has separate units for software development. The company uses AWS Organizations and federation with SAML to give permissions to developers to manage resources in their AWS accounts. The development units each deploy their production workloads into a common production account.
Recently, an incident occurred in the production account in which members of a development unitterminated an EC2 instance that belonged to a different development unit. A solutions architect must create a solution that prevents a similar incident from happening in the future. The solution also must allow developers the possibility to manage the instances used for their workloads.
Which strategy will meet these requirements?

A. Pass an attribute for DevelopmentUnit as an AWS Security Token Service (AWS STS) session tag during SAML federation. Create an SCP with an allow action and a StringEquals condition for the DevelopmentUnit resource tag and aws:PrincipalTag/DevelopmentUnit. Assign the SCP to the root OU.
B. Create separate OUs in AWS Organizations for each development unit. Assign the created OUs to the company AWS accounts. Create separate SCPs with a deny action and a StringNotEquals condition for the DevelopmentUnit resource tag that matches the development unit name. Assign the SCP to the corresponding OU.
C. Pass an attribute for DevelopmentUnit as an AWS Security Token Service (AWS STS) session tag during SAML federation. Update the IAM policy for the developers' assumed IAM role with a deny action and a StringNotEquals condition for the DevelopmentUnit resource tag and aws:PrincipalTag/ DevelopmentUnit.
D. Create separate IAM policies for each development unit. For every IAM policy, add an allow action and a StringEquals condition for the DevelopmentUnit resource tag and the development unit name. During SAML federation, use AWS Security Token Service (AWS STS) to assign the IAM policy and match the development unit name to the assumed IAM role.

Answer: C

Explanation:
This option allows the solutions architect to use session tags to pass additional information about the federated user, such as the development unit name, to AWS1. Session tags are key-value pairs that you can define in your identity provider (IdP) and pass in your SAML assertion1. By using a deny action and a StringNotEquals condition in the IAM policy, you can prevent developers from accessing or modifying EC2 instances that belong to a different development unit2. This way, you can enforce fine-grained access control and prevent accidental or malicious incidents.
Passing session tags in SAML assertions
Using tags for attribute-based access control

NEW QUESTION # 190
A solutions architect has an operational workload deployed on Amazon EC2 instances in an Auto Scaling Group The VPC architecture spans two Availability Zones (AZ) with a subnet in each that the Auto Scaling group is targeting. The VPC is connected to an on-premises environment and connectivity cannot be interrupted The maximum size of the Auto Scaling group is 20 instances in service. The VPC IPv4 addressing is as follows:
VPCCIDR 10 0 0 0/23
AZ1 subnet CIDR: 10 0 0 0724
AZ2 subnet CIDR: 10.0.1 0724
Since deployment, a third AZ has become available in the Region The solutions architect wants to adopt the new AZ without adding additional IPv4 address space and without service downtime. Which solution will meet these requirements?

A. Terminate the EC2 instances in the AZ1 subnet Delete and re-create the AZ1 subnet using hall the address space. Update the Auto Scaling group to use this new subnet. Repeat this for the second AZ.
Define a new subnet in AZ3: then update the Auto Scaling group to target all three new subnets
B. Create a new VPC with the same IPv4 address space and define three subnets, with one for each AZ Update the existing Auto Scaling group to target the new subnets in the new VPC
C. Update the Auto Scaling group to use the AZ2 subnet only Delete and re-create the AZ1 subnet using half the previous address space Adjust the Auto Scaling group to also use the new AZI subnet When the instances are healthy, adjust the Auto Scaling group to use the AZ1 subnet only Remove the current AZ2 subnet Create a new AZ2 subnet using the second half of the address space from the original AZ1 subnet Create a new AZ3 subnet using half the original AZ2 subnet address space, then update the Auto Scaling group to target all three new subnets.
D. Update the Auto Scaling group to use the AZ2 subnet only Update the AZ1 subnet to have halt the previous address space Adjust the Auto Scaling group to also use the AZ1 subnet again. When the instances are healthy, adjust the Auto Seating group to use the AZ1 subnet only. Update the current AZ2 subnet and assign the second half of the address space from the original AZ1 subnet Create a new AZ3 subnet using half the original AZ2 subnet address space, then update the Auto Scaling group to target all three new subnets

Answer: C

Explanation:
https://repost.aws/knowledge-center/vpc-ip-address-range

NEW QUESTION # 191
A North American company with headquarters on the East Coast is deploying a new web application running on Amazon EC2 in the us-east-1 Region. The application should dynamically scale to meet user demand and maintain resiliency. Additionally, the application must have disaster recover capabilities in an active-passive configuration with the us-west-1 Region.
Which steps should a solutions architect take after creating a VPC in the us-east-1 Region?

A. Create a VPC in the us-west-1 Region. Use inter-Region VPC peering to connect both VPCs. Deploy an Application Load Balancer (ALB) spanning multiple Availability Zones (AZs) to the VPC in the us-east-1 Region. Deploy EC2 instances across multiple AZs in each Region as part of an Auto Scaling group spanning both VPCs and served by the ALB.
B. Deploy an Application Load Balancer (ALB) spanning multiple Availability Zones (AZs) to the VPC in the us-east-1 Region. Deploy EC2 instances across multiple AZs as part of an Auto Scaling group served by the ALB. Deploy the same solution to the us-west-1 Region. Create an Amazon Route 53 record set with a failover routing policy and health checks enabled to provide high availability across both Regions.
C. Deploy an Application Load Balancer (ALB) spanning multiple Availability Zones (AZs) to the VPC in the us-east-1 Region. Deploy EC2 instances across multiple AZs as part of an Auto Scaling group served by the ALB. Deploy the same solution to the us-west-1 Region. Create separate Amazon Route
53 records in each Region that point to the ALB in the Region. Use Route 53 health checks to provide high availability across both Regions.
D. Create a VPC in the us-west-1 Region. Use inter-Region VPC peering to connect both VPCs. Deploy an Application Load Balancer (ALB) that spans both VPCs. Deploy EC2 instances across multiple Availability Zones as part of an Auto Scaling group in each VPC served by the ALB. Create an Amazon Route 53 record that points to the ALB.

Answer: B

NEW QUESTION # 192
A solutions architect needs to review the design of an Amazon EMR cluster that is using the EMR File System (EMRFS). The cluster performs tasks that are critical to business needs. The cluster is running Amazon EC2 On-Demand Instances at all times for all task, master, and core nodes.
The EMR tasks run each morning, starting at 1:00 AM, and take 6 hours to finish running. The amount of time to complete the processing is not a priority because the data is not referenced until late in the day. The solutions architect must review the architecture and suggest a solution to minimize the compute costs.
Which solution should the solutions architect recommend to meet these requirements?

A. Continue to launch all nodes on On-Demand Instances.
Terminate the cluster.
Including all instances, when the processing Is completed.
Purchase Compute Savings Plans to cover the On-Demand Instance usage.
B. Launch the master and core nodes on On-Demand Instances.
Launch the task nodes on Spot Instances In an instance fleet.
Terminate only the task node Instances when the processing is completed.
Purchase Compute Savings Plans to cover the On-Demand Instance usage.
C. Launch all task, master, and core nodes on Spot Instances in an instance fleet.
Terminate the cluster, including all instances, when the processing is completed.
D. Launch the master and core nodes on On-Demand Instances.
Launch the task nodes on Spot Instances In an instance fleet.
Terminate the cluster, including all instances, when the processing is completed.
Purchase Compute Savings Plans to cover the On-Demand Instance usage.

Answer: B

Explanation:
The cluster performs tasks that are critical to business needs so we can't turn off cluster.

NEW QUESTION # 193
......

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