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Latest Amazon AWS DAS-C01 practice exam questions at here:

QUESTION 1
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 2
A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5
TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with
2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals.
The company wants to run an additional analysis on a year\\’s worth of historical data to examine trends indicating
which features are most popular. This analysis will be done once a week.
What is the MOST cost-effective solution?
A. Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1 year of
data. Then use Amazon Redshift for the additional analysis.
B. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in
Apache Parquet format partitioned by date. Then use Amazon Redshift Spectrum for the additional analysis.
C. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in
Apache Parquet format partitioned by date. Then provision a persistent Amazon EMR cluster and use Apache Presto for
the additional analysis.
D. Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each
individual node in the Amazon Redshift cluster. Then use Amazon Redshift for the additional analysis.
Correct Answer: B

QUESTION 3
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


QUESTION 4
An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a
large number of small JOSN files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache
Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error
message in the History tab on the AWS Glue console: “Command Failed with Exit Code 1.”
Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe
threshold of 50% usage quickly and reaches 90–95% soon after. The average memory usage across all executors
continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.

DAS-C01 exam questions-q4

What should the data engineer do to solve the failure in the MOST cost-effective way?
A. Change the worker type from Standard to G.2X.
B. Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.
C. Increase the fetch size setting by using AWS Glue dynamics frame.
D. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.
Correct Answer: D


QUESTION 5
A company wants to provide its data analysts with uninterrupted access to the data in its Amazon Redshift cluster. All
data is streamed to an Amazon S3 bucket with Amazon Kinesis Data Firehose. An AWS Glue job that is scheduled to
run every 5 minutes issues a COPY command to move the data into Amazon Redshift.
The amount of data delivered is uneven throughout the day, and cluster utilization is high during certain periods. The
COPY command usually completes within a couple of seconds. However, when load spike occurs, locks can exist and
data can be missed. Currently, the AWS Glue job is configured to run without retries, with timeout at 5 minutes and
concurrency at 1.
How should a data analytics specialist configure the AWS Glue job to optimize fault tolerance and improve data
availability in the Amazon Redshift cluster?
A. Increase the number of retries. Decrease the timeout value. Increase the job concurrency.
B. Keep the number of retries at 0. Decrease the timeout value. Increase the job concurrency.
C. Keep the number of retries at 0. Decrease the timeout value. Keep the job concurrency at 1.
D. Keep the number of retries at 0. Increase the timeout value. Keep the job concurrency at 1.
Correct Answer: B

QUESTION 6
A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR)
data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year
and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data
Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.
When defining tables in the Data Catalog, the company has the following requirements:
1.
Choose the catalog table name and do not rely on the catalog table naming algorithm.
2.
Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.
Which solution meets these requirements with minimal effort?
A. Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes
tables in the Data Catalog.
B. Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to
update the table partitions hourly.
C. Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler
and specify the table as the source.
D. Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table
partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.
Correct Answer: B
Reference: https://docs.aws.amazon.com/glue/latest/dg/tables-described.html


QUESTION 7
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 8
A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into
Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently,
the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue
job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?
A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
B. Enable job bookmarks on the AWS Glue jobs.
C. Create custom logic on the ETL jobs to track the processed S3 objects.
D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.
Correct Answer: D


QUESTION 9
A company is planning to do a proof of concept for a machine earning (ML) project using Amazon SageMaker with a
subset of existing on-premises data hosted in the company\\’s 3 TB data warehouse. For part of the project, AWS Direct
Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data
analysts want to perform multiple-step, including mapping, dropping null fields, resolving choice, and splitting fields. The
company needs the fastest solution to curate the data for this project.
Which solution meets these requirements?
A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon
EMR cluster. Store the curated data in Amazon S3 for ML processing.
B. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML
processing.
C. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon
3 for ML processing.
D. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball
data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.
Correct Answer: C

QUESTION 10
A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense
storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into
Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O
intensive resources. The company needs to adjust the cluster\\’s capacity to support the change in analytical and
storage requirements.
Which solution meets these requirements?
A. Resize the cluster using elastic resize with dense compute nodes.
B. Resize the cluster using classic resize with dense compute nodes.
C. Resize the cluster using elastic resize with dense storage nodes.
D. Resize the cluster using classic resize with dense storage nodes.
Correct Answer: C
Reference: https://aws.amazon.com/redshift/pricing/


QUESTION 11
A bank operates in a regulated environment. The compliance requirements for the country in which the bank operates
say that customer data for each state should only be accessible by the bank\\’s employees located in the same state.
Bank employees in one state should NOT be able to access data for customers who have provided a home address in a
different state.
The bank\\’s marketing team has hired a data analyst to gather insights from customer data for a new campaign being
launched in certain states. Currently, data linking each customer account to its home state is stored in a tabular .csv file
within a single Amazon S3 folder in a private S3 bucket. The total size of the S3 folder is 2 GB uncompressed. Due to
the country\\’s compliance requirements, the marketing team is not able to access this folder.
The data analyst is responsible for ensuring that the marketing team gets one-time access to customer data for their
campaign analytics project, while being subject to all the compliance requirements and controls.
Which solution should the data analyst implement to meet the desired requirements with the LEAST amount of setup
effort?
A. Re-arrange data in Amazon S3 to store customer data about each state in a different S3 folder within the same
bucket. Set up S3 bucket policies to provide marketing employees with appropriate data access under compliance
controls. Delete the bucket policies after the project.
B. Load tabular data from Amazon S3 to an Amazon EMR cluster using s3DistCp. Implement a custom Hadoop-based
row-level security solution on the Hadoop Distributed File System (HDFS) to provide marketing employees with
appropriate data access under compliance controls. Terminate the EMR cluster after the project.
C. Load tabular data from Amazon S3 to Amazon Redshift with the COPY command. Use the built-in row-level security
feature in Amazon Redshift to provide marketing employees with appropriate data access under compliance controls.
Delete the Amazon Redshift tables after the project.
D. Load tabular data from Amazon S3 to Amazon QuickSight Enterprise edition by directly importing it as a data source.
Use the built-in row-level security feature in Amazon QuickSight to provide marketing employees with appropriate data
access under compliance controls. Delete Amazon QuickSight data sources after the project is complete.
Correct Answer: C

QUESTION 12
A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join
data stored in Amazon S3 in Apache ORC format with data stored in Amazon Elasticsearch Service (Amazon ES) and
Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?
A. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon
Athena.
B. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with
Amazon Redshift.
C. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.
D. Query all the datasets in place with Apache Presto running on Amazon EMR.
Correct Answer: C
QUESTION 13
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 a 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: https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hbase-s3.html

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