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Amazon AWS Certified Solutions Architect - Professional Sample Questions (Q302-Q307):

NEW QUESTION # 302
An organization is creating a VPC for their application hosting. The organization has created two private
subnets in the same AZ and created one subnet in a separate zone. The organization wants to make a
HA system with the internal ELB. Which of these statements is true with respect to an internal ELB in this
scenario?

• A. ELB can support all the subnets irrespective of their zones.
• B. If the user is creating an internal ELB, he should use only private subnets.
• C. ELB does not allow subnet selection; instead it will automatically select all the available subnets of the
  VPC.
• D. ELB can support only one subnet in each availability zone.

Answer: D Explanation:
The Amazon Virtual Private Cloud (Amazon VPC) allows the user to define a virtual networking
environment in a private, isolated section of the Amazon Web Services (AWS) cloud. The user has
complete control over the virtual networking environment. Within this virtual private cloud, the user can
launch AWS resources, such as an ELB, and EC2 instances.
There are two ELBs available with VPC: internet facing and internal (private) ELB. For internal servers,
such as App servers the organization can create an internal load balancer in their VPC and then place
back-end application instances behind the internal load balancer. The internal load balancer will route
requests to the back-end application instances, which are also using private IP addresses and only accept
requests from the internal load balancer.
The Internal ELB supports only one subnet in each AZ and asks the user to select a subnet while
configuring internal ELB.
Reference:
http://docs.aws.amazon.com/ElasticLoadBalancing/latest/DeveloperGuide/USVPCcreatingbasic_lb.ht
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NEW QUESTION # 303
Attempts, one of the three types of items associated with the schedule pipeline in the AWS Data Pipeline,
provides robust data management.
Which of the following statements is NOT true about Attempts?

• A. AWS Data Pipeline Attempt objects track the various attempts, results, and failure reasons if
  applicable.
• B. AWS Data Pipeline retries a failed operation until the count of retries reaches the maximum number of
  allowed retry attempts.
• C. Attempts provide robust data management.
• D. An AWS Data Pipeline Attempt object compiles the pipeline components to create a set of actionable
  instances.

Answer: D Explanation:
Attempts, one of the three types of items associated with a schedule pipeline in AWS Data Pipeline,
provides robust data management. AWS Data Pipeline retries a failed operation. It continues to do so until
the task reaches the maximum number of allowed retry attempts. Attempt objects track the various
attempts, results, and failure reasons if applicable. Essentially, it is the instance with a counter. AWS Data
Pipeline performs retries using the same resources from the previous attempts, such as Amazon EMR
clusters and EC2 instances.
Reference:
http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-how-tasks-scheduled.html
NEW QUESTION # 304
A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data and uses the S3 Standard storage class. The data increases in size by several gigabytes every day.
The company needs to query and analyze the data. The company does not access data that is more than 1 year old. However, the company must retain all the data indefinitely for compliance reasons.
Which solution will meet these requirements MOST cost-effectively?

• A. Use an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
• B. Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Intelligent-Tiering.
• C. Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.
• D. Use S3 Select to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.

Answer: D Explanation:
Explanation
S3 Select allows you to query the data stored in an S3 bucket, which can be useful when you need to retrieve specific subsets of data from a large amount of data. By creating an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive, you can save cost as it is a low-cost storage class for archival data that is infrequently accessed and for which retrieval times of several hours are acceptable. This solution is most cost-effective as it allows you to keep all the data indefinitely for compliance reasons while also reducing storage costs for older data that is not frequently accessed.
Using S3 Select to query the data and an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive will minimize the cost of storing and querying the data. S3 Select allows the company to retrieve only the data that is needed for a specific query, which reduces the amount of data that needs to be retrieved and processed, which can help lower costs. S3 Glacier Deep Archive is the lowest cost storage class and will save cost for storing data that is not accessed frequently.
Reference:
https://aws.amazon.com/s3/features/select/
https://aws.amazon.com/s3/storage-classes/
https://aws.amazon.com/glacier/deep-archive/
https://aws.amazon.com/s3/lifecycle/
NEW QUESTION # 305
An enterprise company is building an infrastructure services platform for its users. The company has the following requirements:
* Provide least privilege access to users when launching AWS infrastructure so users cannot provision unapproved services
* Use a central account to manage the creation of infrastructure services
* Provide the ability to distribute infrastructure services to multiple accounts in AWS Organizations
* Provide the ability to enforce tags on any infrastructure that is started by users Which combination of actions using AWS services will meet these requirements? (Select THREE.)

• A. Use the AWS CloudFormation Resource Tags property to enforce the application of tags to any CloudFormation templates that will be created for users
• B. Allow user 1AM roles to have AWSCIoudFormationFullAccess and AmazonS3ReadOnlyAccess permissions Add an Organizations SCP at the AWS account root user level to deny all services except AWS CloudFormation and Amazon S3.
• C. Allow user 1AM roles to have ServiceCatalogEndUserAccess permissions only Use an automation script to import the central portfolios to local AWS accounts, copy the TagOption assign users access and apply launch constraints
• D. Use the AWS Service Catalog TagOption Library to maintain a list of tags required by the company Apply the TagOption to AWS Service Catalog products or portfolios
• E. Develop infrastructure services using AWS Cloud For matron templates Upload each template as an AWS Service Catalog product to portfolios created in a central AWS account Share these portfolios with the Organizations structure created for the company
• F. Develop infrastructure services using AWS Cloud Formation templates Add the templates to a central Amazon S3 bucket and add the-IAM rotes or users that require access to the S3 bucket policy

Answer: D,E,F
NEW QUESTION # 306
A company has several teams, and each team has their own Amazon RDS database that totals 100 TB The company is building a data query platform for Business Intelligence Analysts to generate a weekly business report The new system must run ad-hoc SQL queries What is the MOST cost-effective solution?

• A. Use an AWS Glue crawler to crawl all the databases and create tables in the AWS Glue Data Catalog Use an AWS Glue ETL Job to load data from the RDS databases to Amazon S3, and use Amazon Athena to run the queries.
• B. Create an Amazon EMR cluster with enough core nodes Run an Apache Spark job to copy data from the RDS databases to an Hadoop Distributed File System (HDFS) Use a local Apache Hive metastore to maintain the table definition Use Spark SQL to run the query
• C. Create a new Amazon Redshift cluster Create an AWS Glue ETL job to copy data from the RDS databases to the Amazon Redshift cluster Use Amazon Redshift to run the query
• D. Use an AWS Glue ETL job to copy all the RDS databases to a single Amazon Aurora PostgreSQL database Run SQL queries on the Aurora PostgreSQL database

Answer: A
NEW QUESTION # 307
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