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Latest Amazon DAS-C01 practice exam questions

QUESTION 1
A financial services company needs to aggregate daily stock trade data from the exchanges into a data store. The
company requires that data be streamed directly into the data store, but also occasionally allows data to be modified
using SQL. The solution should integrate complex, analytic queries running with minimal latency. The solution must
provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
Which solution meets the company\\’s requirements?
A. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon
QuickSight to create a business intelligence dashboard.
B. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for
Amazon QuickSight to create a business intelligence dashboard.
C. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for
Amazon QuickSight to create a business intelligence dashboard.
D. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon
QuickSight to create a business intelligence dashboard.
Correct Answer: D

QUESTION 2
A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50
business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared
with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by
year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main
data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from
at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?
A. Load the data into an Amazon Redshift cluster by using the COPY command. Configure 50 author users and 1,000
reader users. Use QuickSight Enterprise edition. Configure an Amazon Redshift data source with a direct query option.
B. Use QuickSight Standard edition. Configure 50 author users and 1,000 reader users. Configure an Athena data
source with a direct query option.
C. Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data
source and import the data into SPICE. Automatically refresh every 24 hours.
D. Use QuickSight Enterprise edition. Configure 1 administrator and 1,000 reader users. Configure an S3 data source
and import the data into SPICE. Automatically refresh every 24 hours.
Correct Answer: C

QUESTION 3
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 4
A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex
real-word scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the
LEAST amount of management overhead.
Which solution will meet these requirements?
A. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business
metrics.
C. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize
the data.
D. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.
Correct Answer: A
Reference: https://aws.amazon.com/blogs/big-data/query-visualize-and-forecast-trufactor-web-sessionintelligence-withaws-data-exchange/

QUESTION 5
An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities.
Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an
application running on Amazon EC2 processes the data and makes search options and reports available for
visualization by editors and marketers. The company wants to make website clicks and aggregated data available to
editors and marketers in minutes to enable them to connect with users more effectively.
Which options will help meet these requirements in the MOST efficient way? (Choose two.)
A. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch
Service.
B. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon
Elasticsearch Service from Amazon S3.
C. Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh
content performance dashboards in near-real time.
D. Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content
performance dashboards in near-real time.
E. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams
consumer to send records to Amazon Elasticsearch Service.
Correct Answer: CE

QUESTION 6
A company has a data lake on AWS that ingests sources of data from multiple business units and uses Amazon Athena
for queries. The storage layer is Amazon S3 using the AWS Glue Data Catalog. The company wants to make the data
available to its data scientists and business analysts. However, the company first needs to manage data access for
Athena based on user roles and responsibilities.
What should the company do to apply these access controls with the LEAST operational overhead?
A. Define security policy-based rules for the users and applications by role in AWS Lake Formation.
B. Define security policy-based rules for the users and applications by role in AWS Identity and Access Management
(IAM).
C. Define security policy-based rules for the tables and columns by role in AWS Glue.
D. Define security policy-based rules for the tables and columns by role in AWS Identity and Access Management
(IAM).
Correct Answer: D

QUESTION 7
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 8
A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive
data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data
must be encrypted through a hardware security module (HSM) with automated key rotation.
Which combination of steps is required to achieve compliance? (Choose two.)
A. Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.
B. Modify the cluster with an HSM encryption option and automatic key rotation.
C. Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.
D. Enable HSM with key rotation through the AWS CLI.
E. Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.
Correct Answer: BD
Reference: https://docs.aws.amazon.com/redshift/latest/mgmt/working-with-db-encryption.html

QUESTION 9
A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The
application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon
CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?
A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to
transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table.
Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
B. Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the
logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data.
Store the enriched data in Amazon S3.
C. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis
Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the
SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using
Amazon Kinesis Data Firehose.
D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR
to read the logs from Amazon S3 and enrich the records with the data from DynamoDB. Store the enriched data in
Amazon S3.
Correct Answer: C

QUESTION 10
A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in
through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support nearreal-time data.
Which visualization solution will meet these requirements?
A. 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.
B. 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.
C. 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.
D. 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.
Correct Answer: A

QUESTION 11
A company needs to store objects containing log data in JSON format. The objects are generated by eight applications
running in AWS. Six of the applications generate a total of 500 KiB of data per second, and two of the applications can
generate up to 2 MiB of data per second.
A data engineer wants to implement a scalable solution to capture and store usage data in an Amazon S3 bucket. The
usage data objects need to be reformatted, converted to .csv format, and then compressed before they are stored in
Amazon S3. The company requires the solution to include the least custom code possible and has authorized the data
engineer to request a service quota increase if needed.
Which solution meets these requirements?
A. Configure an Amazon Kinesis Data Firehose delivery stream for each application. Write AWS Lambda functions to
read log data objects from the stream for each application. Have the function perform reformatting and .csv conversion.
Enable compression on all the delivery streams.
B. Configure an Amazon Kinesis data stream with one shard per application. Write an AWS Lambda function to read
usage data objects from the shards. Have the function perform .csv conversion, reformatting, and compression of the
data. Have the function store the output in Amazon S3.
C. Configure an Amazon Kinesis data stream for each application. Write an AWS Lambda function to read usage data
objects from the stream for each application. Have the function perform .csv conversion, reformatting, and compression
of the data. Have the function store the output in Amazon S3.
D. Store usage data objects in an Amazon DynamoDB table. Configure a DynamoDB stream to copy the objects to an
S3 bucket. Configure an AWS Lambda function to be triggered when objects are written to the S3 bucket. Have the
function convert the objects into .csv format.
Correct Answer: B

QUESTION 12
An online retail company is migrating its reporting system to AWS. The company\\’s legacy system runs data processing
on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the
online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To
keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports
and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?
A. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an AWS Glue crawler over Amazon S3 that
runs when data is refreshed to ensure that data changes are updated. Create an Amazon EMR cluster and use the
metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
B. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an Amazon EMR cluster with consistent
view enabled. Run emrfs sync before each analytics step to ensure data changes are updated. Create an EMR cluster
and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.
C. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from
underlying queries against the raw dataset. Create an AWS Glue Data Catalog to manage the Hive metadata over the
CTAS table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive
processing queries in Amazon EMR.
D. Use an S3 Select query to ensure that the data is properly updated. Create an AWS Glue Data Catalog to manage
the Hive metadata over the S3 Select table. Create an Amazon EMR cluster and use the metadata in the AWS Glue
Data Catalog to run Hive processing queries in Amazon EMR.
Correct Answer: A

QUESTION 13
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

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