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SAP-C01 PDF DEMO:

QUESTION NO: 1
A company wants to migrate its website from an on-premises data center onto AWS. At the same time, it wants to migrate the website to a containerized microservice-based architecture to improve the availability and cost efficiency. The company's security policy states that privileges and network permissions must be configured according to best practice, using least privilege.
A Solutions Architect must create a containerized architecture that meets the security requirements and has deployed the application to an Amazon ECS cluster.
What steps are required after the deployment to meet the requirements? (Choose two.)
A. Apply security groups to the tasks, and use IAM roles for tasks to access other resources.
B. Apply security groups to Amazon EC2 instances, and use IAM roles for EC2 instances to access other resources.
C. Create tasks using the awsvpc network mode.
D. Create tasks using the bridge network mode.
E. Apply security groups to the tasks, and pass IAM credentials into the container at launch time to access other resources.
Answer: A,C
Explanation
https://aws.amazon.com/about-aws/whats-new/2017/11/amazon-ecs-introduces-awsvpc- networking-mode-for-co
https://amazonaws-china.com/blogs/compute/introducing-cloud-native-networking-for-ecs- containers/
https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-iam-roles.html

QUESTION NO: 2
A company collects a steady stream of 10 million data records from 100,000 sources each day. These records are written to an Amazon RDS MySQL DB. A query must produce the daily average of a data source over the past 30 days. There are twice as many reads as writes. Queries to the collected data are for one source ID at a time.
How can the Solutions Architect improve the reliability and cost effectiveness of this solution?
A. Use Amazon DynamoDB with the source ID as the partition key and the timestamp as the sort key.
Use a Time to Live (TTL) to delete data after 30 days.
B. Use Amazon Aurora with MySQL in a Multi-AZ mode. Use four additional read replicas.
C. Ingest data into Amazon Kinesis using a retention period of 30 days. Use AWS Lambda to write data records to Amazon ElastiCache for read access.
D. Use Amazon DynamoDB with the source ID as the partition key. Use a different table each day.
Answer: A
Explanation
https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html

QUESTION NO: 3
A company that provides wireless services needs a solution to store and analyze log files about user activities.
Currently, log files are delivered daily to Amazon Linux on Amazon EC2 instance. A batch script is run once a day to aggregate data used for analysis by a third-party tool. The data pushed to the third- party tool is used to generate a visualization for end users. The batch script is cumbersome to maintain, and it takes several hours to deliver the ever-increasing data volumes to the third-party tool. The company wants to lower costs, and is open to considering a new tool that minimizes development effort and lowers administrative overhead. The company wants to build a more agile solution that can store and perform the analysis in near-real time, with minimal overhead. The solution needs to be cost effective and scalable to meet the company's end-user base growth.
Which solution meets the company's requirements?
A. Use an Amazon Kinesis agent running on an EC2 instance to collect and send the data to an
Amazon Kinesis Data Firehose delivery stream. The Kinesis Data Firehose delivery stream will deliver the data to Amazon S3. Use an AWS Lambda function to deliver the data from Amazon S3 to Amazon
ES. Use Kibana to visualize the data.
B. Use an Amazon Kinesis agent running on an EC2 instance in an Auto Scaling group to collect and send the data to an Amazon Kinesis Data Forehose delivery stream. The Kinesis Data Firehose delivery stream will deliver the data directly to Amazon ES. Use Kibana to visualize the data.
C. Use an in-memory caching application running on an Amazon EBS-optimized EC2 instance to capture the log data in near real-time. Install an Amazon ES cluster on the same EC2 instance to store the log files as they are delivered to Amazon EC2 in near real-time. Install a Kibana plugin to create the visualizations.
D. Develop a Python script to failure the data from Amazon EC2 in real time and store the data in
Amazon S3. Use a copy command to copy data from Amazon S3 to Amazon Redshift. Connect a business intelligence tool running on Amazon EC2 to Amazon Redshift and create the visualizations.
Answer: B
Explanation
https://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html

QUESTION NO: 4
A company has an application that runs a web service on Amazon EC2 instances and stores .jpg images in Amazon S3. The web traffic has a predictable baseline, but often demand spikes unpredictably for short periods of time. The application is loosely coupled and stateless. The .jpg images stored in Amazon S3 are accessed frequently for the first 15 to 20 days, they are seldom accessed thereafter but always need to be immediately available. The CIO has asked to find ways to reduce costs.
Which of the following options will reduce costs? (Choose two.)
A. Create a script that checks the load on all web servers and terminates unnecessary On-Demand instances.
B. Configure a lifecycle policy to move the .jpg images on Amazon S3 to S3 IA after 30 days.
C. Configure a lifecycle policy to move the .jpg images on Amazon S3 to Amazon Glacier after 30 days.
D. Purchase Reserved instances for baseline capacity requirements and use On-Demand instances for the demand spikes.
E. Use On-Demand instances for baseline capacity requirements and use Spot Fleet instances for the demand spikes.
Answer: B,D

QUESTION NO: 5
A company has a large on-premises Apache Hadoop cluster with a 20 PB HDFS database. The cluster is growing every quarter by roughly 200 instances and 1 PB. The company's goals are to enable resiliency for its Hadoop data, limit the impact of losing cluster nodes, and significantly reduce costs. The current cluster runs 24/7 and supports a variety of analysis workloads, including interactive queries and batch processing.
Which solution would meet these requirements with the LEAST expense and down time?
A. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent
Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on
Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
B. Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent
Amazon EMR cluster of similar size and configuration to the current cluster. Store the data on EMRFS.
Minimize costs by using Reserved Instances. As the workload grows each quarter, purchase additional
Reserved Instances and add to the cluster.
C. Use AWS Direct Connect to migrate the existing cluster data to Amazon S3. Create a persistent
Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on
Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
D. Use AWS Snowball to migrate the existing cluster data to Amazon S3. Create a persistent Amazon
EMR cluster initially sized to handle the interactive workloads based on historical data from the on- premises cluster. Store the on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Answer: A
Explanation
Q: How should I choose between Snowmobile and Snowball?
To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. In addition, you should evaluate the amount of available bandwidth in your network backbone. If you have a high speed backbone with hundreds of Gb/s of spare throughput, then you can use Snowmobile to migrate the large datasets all at once. If you have limited bandwidth on your backbone, you should consider using multiple Snowballs to migrate the data incrementally.

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Updated: May 28, 2022

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