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  • Exam Name: AWS Certified Data Analytics - Specialty
  • Last Update: 22-Apr-2024
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DAS-C01 Questions and Answers

Question # 1

A company developed a new elections reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a low-cost option to perform this infrequent data analysis with visualizations of logs in a way that requires minimal development effort.

Which solution meets these requirements?

A.

Use an AWS Glue crawler to create and update a table in the Glue data catalog from the logs. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

B.

Create a second Kinesis Data Firehose delivery stream to deliver the log files to Amazon Elasticsearch Service (Amazon ES). Use Amazon ES to perform text-based searches of the logs for ad-hoc analyses and use Kibana for data visualizations.

C.

Create an AWS Lambda function to convert the logs into .csv format. Then add the function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.

D.

Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Question # 2

A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most cost-effective solution.

Which approach meets these requirements for optimizing and querying the log data?

A.

Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.

B.

Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and day. Use Apache Presto to query the optimized format.

C.

Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and day. Use Amazon Redshift Spectrum to query the data.

D.

Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query

data.

Question # 3

A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehose. The company is using 10 MB as the S3 buffer size and is using 90 seconds as the buffer interval. The company runs an AWS Glue ET L job to merge and transform the data to a different format before writing the data back to Amazon S3.

Recently, the company has experienced substantial growth in its data volume. The AWS Glue ETL jobs are frequently showing an OutOfMemoryError error.

Which solutions will resolve this issue without incurring additional costs? (Select TWO.)

A.

Place the small files into one S3 folder. Define one single table for the small S3 files in AWS Glue Data Catalog. Rerun the AWS Glue ET L jobs against this AWS Glue table.

B.

Create an AWS Lambda function to merge small S3 files and invoke them periodically. Run the AWS Glue ETL jobs after successful completion of the Lambda function.

C.

Run the S3DistCp utility in Amazon EMR to merge a large number of small S3 files before running the AWS Glue ETL jobs.

D.

Use the groupFiIes setting in the AWS Glue ET L job to merge small S3 files and rerun AWS Glue E TL jobs.

E.

Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.

Question # 4

An online retail company uses Amazon Redshift to store historical sales transactions. The company is required to encrypt data at rest in the clusters to comply with the Payment Card Industry Data Security Standard (PCI DSS). A corporate governance policy mandates management of encryption keys using an on-premises hardware security module (HSM).

Which solution meets these requirements?

A.

Create and manage encryption keys using AWS CloudHSM Classic. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management.

B.

Create a VPC and establish a VPN connection between the VPC and the on-premises network. Create an HSM connection and client certificate for the on-premises HSM. Launch a cluster in the VPC with the option to use the on-premises HSM to store keys.

C.

Create an HSM connection and client certificate for the on-premises HSM. Enable HSM encryption on the existing unencrypted cluster by modifying the cluster. Connect to the VPC where the Amazon Redshift cluster resides from the on-premises network using a VPN.

D.

Create a replica of the on-premises HSM in AWS CloudHSM. Launch a cluster in a VPC with the option to use CloudHSM to store keys.

Question # 5

A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis Data Streams to collect the data streams from smart meters. The consumer application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.

The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.

Which solution meets these requirements?

A.

Use enhanced fan-out in Kinesis Data Streams.

B.

Increase the number of shards for the Kinesis data stream.

C.

Reduce the propagation delay by overriding the KCL default settings.

D.

Develop consumers by using Amazon Kinesis Data Firehose.

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