Free Amazon AWS-Certified-Data-Analytics-Specialty Exam Dumps Questions & Answers

A company is creating a data lake by using AWS Lake Formation. The data that will be stored in the data lake contains sensitive customer information and must be encrypted at rest using an AWS Key Management Service (AWS KMS) customer managed key to meet regulatory requirements.
How can the company store the data in the data lake to meet these requirements?

• A.  Store the data in an Amazon S3 bucket by using server-side encryption with AWS KMS (SSE-KMS). Register the S3 location with Lake Formation.
• B.  Store the data in an Amazon S3 Glacier Flexible Retrieval vault bucket. Register the S3 Glacier Flexible Retrieval vault with Lake Formation.
• C.  Store the data in an encrypted Amazon Elastic Block Store (Amazon EBS) volume. Register the Amazon EBS volume with Lake Formation.
• D.  Encrypt the data on the client side and store the encrypted data in an Amazon S3 bucket. Register the S3 location with Lake Formation.

A manufacturing company has many loT devices in different facilities across the world The company is using Amazon Kinesis Data Streams to collect the data from the devices The company's operations team has started to observe many WnteThroughputExceeded exceptions The operations team determines that the reason is the number of records that are being written to certain shards The data contains device ID capture date measurement type, measurement value and facility ID The facility ID is used as the partition key Which action will resolve this issue?

• A.  Archive the data on the producers' side
• B.  Increase the number of shards
• C.  Change the partition key from facility ID to capture date
• D.  Change the partition key from facility ID to a randomly generated key

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.
A trips fact table for information on completed rides. A drivers dimension table for driver profiles.
A customers fact table holding customer profile information.
The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes.
What table design provides optimal query performance?

• A.  Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
• B.  Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
• C.  Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
• D.  Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

A technology company is creating a dashboard that will visualize and analyze time-sensitive dat a. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?

• A.  Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
• B.  Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehose. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
• C.  Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.
• D.  Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.

A data engineering team within a shared workspace company wants to build a centralized logging system for all weblogs generated by the space reservation system. The company has a fleet of Amazon EC2 instances that process requests for shared space reservations on its website. The data engineering team wants to ingest all weblogs into a service that will provide a near-real-time search engine. The team does not want to manage the maintenance and operation of the logging system.
Which solution allows the data engineering team to efficiently set up the web logging system within AWS?

• A.  Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Data Firehose delivery stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.
• B.  Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Configure Splunk as the end destination of the weblogs.
• C.  Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis Firehose delivery stream to CloudWatch. Configure Amazon DynamoDB as the end destination of the weblogs.
• D.  Set up the Amazon CloudWatch agent to stream weblogs to CloudWatch logs and subscribe the Amazon Kinesis data stream to CloudWatch. Choose Amazon Elasticsearch Service as the end destination of the weblogs.