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Amazon DAS-C01 Exam Dumps

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Amazon DAS-C01 Practice Test 1-13

QUESTION 1
A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data
stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading
the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the
order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more
than an hour to appear in the Elasticsearch cluster.
What should the data analyst do reduce this latency?
A. Migrate the validation process to Amazon Kinesis Data Firehose.
B. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
C. Increase the number of shards in the stream.
D. Configure multiple Lambda functions to process the stream.
Correct Answer: C


QUESTION 2
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis
and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and
resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for
improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion
events every day.
How should this data be stored for optimal performance?
A. In Apache ORC partitioned by date and sorted by source IP
B. In compressed .csv partitioned by date and sorted by source IP
C. In Apache Parquet partitioned by source IP and sorted by date
D. In compressed nested JSON partitioned by source IP and sorted by date
Correct Answer: D


QUESTION 3
A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and
stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data
warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve data loading performance?
A. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
C. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.
D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
Correct Answer: C
Reference: click here 

QUESTION 4
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake.
There are two data transformation requirements that will enable the consumers within the company to create reports:
1.
Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
2.
One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company\\’s requirements for transforming the data? (Choose
three.)
A. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
B. For daily incoming data, use Amazon Athena to scan and identify the schema.
C. For daily incoming data, use Amazon Redshift to perform transformations.
D. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.
E. For archived data, use Amazon EMR to perform data transformations.
F. For archived data, use Amazon SageMaker to perform data transformations.
Correct Answer: BCD


QUESTION 5
A global company has different sub-organizations, and each sub-organization sells its products and services in various
countries. The company\\’s senior leadership wants to quickly identify which sub-organization is the strongest performer
in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?
A. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.
B. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.
C. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.
D. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.
Correct Answer: C

QUESTION 6
A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third-party
libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to
replace the manual process.
Which options can fulfill these requirements? (Choose two.)
A. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.
B. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.
C. Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and
use that custom AMI to re-create the EMR cluster.
D. Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with
DynamoDB Streams to install the software.
E. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance.
Create an AMI and use that AMI to create the EMR cluster.
Correct Answer: AC


QUESTION 7
A company is building a data lake and needs to ingest data from a relational database that has time-series data. The
company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring
incremental data only from the source into Amazon S3.
What is the MOST cost-effective approach to meet these requirements?
A. Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job
bookmarks.
B. Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon
DynamoDB table and ingest the data using the updated key as a filter.
C. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate
Apache Spark libraries to compare the dataset, and find the delta.
D. Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to
ensure the delta only is written into Amazon S3.
Correct Answer: B

QUESTION 8
A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-realtime solution that can ingest the data securely at scale. The solution should also be able to remove the patient\\’s
protected health information (PHI) from the streaming data and store the data in durable storage.
Which solution meets these requirements with the least operational overhead?
A. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client
Library (KCL) to remove all PHI. Write the data in Amazon S3.
B. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an
AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
C. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an
AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
D. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation
AWS Lambda function that parses the sensor data to remove all PHI.
Correct Answer: C
Reference: click here 
 

QUESTION 9
A media content company has a streaming playback application. The company wants to collect and analyze the data to
provide near-real-time feedback on playback issues. The company needs to consume this data and return results within
30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback
issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over
time.
Which solution will allow the company to collect data for processing while meeting these requirements?
A. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event trigger an AWS
Lambda function to process the data. The Lambda function will consume the data and process it to identify potential
playback issues. Persist the raw data to Amazon S3.
B. Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java
application as the consumer. The application will consume the data and process it to identify potential playback issues.
Persist the raw data to Amazon DynamoDB.
C. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an
event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential
playback issues. Persist the raw data to Amazon DynamoDB.
D. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as
the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw
data to Amazon S3.
Correct Answer: B

QUESTION 10
A financial company uses Amazon S3 as its data lake and has set up a data warehouse using a multi-node Amazon
Redshift cluster. The data files in the data lake are organized in folders based on the data source of each data file. All
the data files are loaded to one table in the Amazon Redshift cluster using a separate COPY command for each data file
location. With this approach, loading all the data files into Amazon Redshift takes a long time to complete. Users want a
faster solution with little or no increase in cost while maintaining the segregation of the data files in the S3 data lake.
Which solution meets these requirements?
A. Use Amazon EMR to copy all the data files into one folder and issue a COPY command to load the data into Amazon
Redshift.
B. Load all the data files in parallel to Amazon Aurora, and run an AWS Glue job to load the data into Amazon Redshift.
C. Use an AWS Glue job to copy all the data files into one folder and issue a COPY command to load the data into
Amazon Redshift.
D. Create a manifest file that contains the data file locations and issue a COPY command to load the data into Amazon
Redshift.
Correct Answer: A
Reference: click here
 

QUESTION 11
A company is migrating its existing on-premises ETL jobs to Amazon EMR. The code consists of a series of jobs written
in Java. The company needs to reduce overhead for the system administrators without changing the underlying code.
Due to the sensitivity of the data, compliance requires that the company use root device volume encryption on all nodes
in the cluster. Corporate standards require that environments be provisioned though AWS CloudFormation when
possible.
Which solution satisfies these requirements?
A. Install open-source Hadoop on Amazon EC2 instances with encrypted root device volumes. Configure the cluster in
the CloudFormation template.
B. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a
bootstrap action to enable TLS.
C. Create a custom AMI with encrypted root device volumes. Configure Amazon EMR to use the custom AMI using the
CustomAmild property in the CloudFormation template.
D. Use a CloudFormation template to launch an EMR cluster. In the configuration section of the cluster, define a
bootstrap action to encrypt the root device volume of every node.
Correct Answer: C
Reference: click here
 

QUESTION 12
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is
configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System
(HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company\\’s requirements?
A. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node. Configure the EMR
cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
B. Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view. Create an EMR
HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.
C. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view. Run two
separate EMR clusters in two different Availability Zones. Point both clusters to the same HBase root directory in the
same Amazon S3 bucket.
D. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view. Create a
primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase read-replica cluster in a
separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
Correct Answer: C
Reference: click here 


QUESTION 13
A company has 1 million scanned documents stored as image files in Amazon S3. The documents contain typewritten
application forms with information including the applicant first name, applicant last name, application date, application
type, and application text. The company has developed a machine learning algorithm to extract the metadata values
from the scanned documents. The company wants to allow internal data analysts to analyze and find applications using
the applicant name, application date, or application text. The original images should also be downloadable. Cost control
is secondary to query performance.
Which solution organizes the images and metadata to drive insights while meeting the requirements?
A. For each image, use object tags to add the metadata. Use Amazon S3 Select to retrieve the files based on the
applicant name and application date.
B. Index the metadata and the Amazon S3 location of the image file in Amazon Elasticsearch Service. Allow the data
analysts to use Kibana to submit queries to the Elasticsearch cluster.
C. Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift table. Allow the data
analysts to run ad-hoc queries on the table.
D. Store the metadata and the Amazon S3 location of the image files in an Apache Parquet file in Amazon S3, and
define a table in the AWS Glue Data Catalog. Allow data analysts to use Amazon Athena to submit custom queries.
Correct Answer: A

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