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Amazon Web Services - DAS-C01
Question #:1
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:
Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled
time.
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.
Answer: B C D
Question #:2
A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon
S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and
manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both
Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?
A.  Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena
queries in us-west-2.
B.  Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run
Athena queries in us-west-2.
C.  Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the
data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog
in us-west-2 and run Athena queries.
D.  Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2.
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D.  
Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.
Answer: C
Question #:3
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company’s data platform
team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job
writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles
column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for
any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?
A.  Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the
existing rows in the main table as postactions in the DynamicFrameWriter class.
B.  Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert
operation in MySQL, and copy the results to the Amazon Redshift table.
C.  Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to
Amazon Redshift.
D.  Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.
Answer: B
Question #:4
A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of
sluggish performance.
A data analyst notes the following:
Approximately 90% of queries are submitted 1 hour after the market opens.
Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?
A.  Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale
out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an
automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB
metric.
B.  Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale
out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch
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B.  
Amazon Web Services - DAS-C01
YARNMemoryAvailablePercentage metric.
C.  Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale
out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an
automatic scaling policy to scale in the instance groups based on the CloudWatch
CapacityRemainingGB metric.
D.  Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale
out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric.
Create an automatic scaling policy to scale in the instance groups based on the CloudWatch
YARNMemoryAvailablePercentage metric.
Answer: C
Question #:5
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000
property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a
subset of the data for a specific vendor.
What is the most cost-effective solution?
A.  Load the data into Amazon S3 and query it with Amazon S3 Select.
B.  Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
C.  Load the data to Amazon S3 and query it with Amazon Athena.
D.  Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.
Answer: C
Question #:6
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.
  
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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.
Answer: C
Question #:7
A regional energy company collects voltage data from sensors attached to buildings. To address any known
dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected
within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are
delivered as quickly as possible. The system must be fully managed and highly available. The company also
needs a solution that will automatically scale up as it covers additional cites with this monitoring feature. The
alerting system is subscribed to an Amazon SNS topic for remediation.
Which solution meets these requirements?
A.  Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark
Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to
process the incoming data. Use the Spark Streaming application to detect the known event sequence and
send the SNS message.
B.  Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function.
Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the
Lambda function, store incoming events in the RDS database and query the latest data to detect the
known event sequence and send the SNS message.
C.  Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an
AWS Lambda transformation function to detect the known event sequence and send the SNS message.
D.  Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for
alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java
application to detect the known event sequence, and add a message to the message stream. Configure an
AWS Lambda function to poll the message stream and publish to the SNS topic.
Answer: D
Question #:8
A team of data scientists plans to analyze market trend data for their company’s new investment strategy. The
trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis
to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications
based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to
Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The
team wants to implement the lowest-cost solution.
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Which solution meets these requirements?
A.  Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library
(KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose
on the Kinesis data stream to persist data to an S3 bucket.
B.  Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing
trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
C.  Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing
trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
D.  Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library
(KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure
Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
Answer: A
Question #:9
A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data
analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same
Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the
data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further
investigation, the data analyst discovers the messages that are out of order seem to be arriving from different
shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?
A.  There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst
needs to make sure there is only a single shard in the stream and no stream resize runs.
B.  The hash key generation process for the records is not working correctly. The data analyst should
generate an explicit hash key on the producer side so the records are directed to the appropriate shard
accurately.
C.  The records are not being received by Kinesis Data Streams in order. The producer should use the
PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering
parameter.
D.  The consumer is not processing the parent shard completely before processing the child shards after a
stream resize. The data analyst should process the parent shard completely first before processing the
child shards.
Answer: A
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Question #:10
A company wants to improve user satisfaction for its smart home system by adding more features to its
recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data
Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that,
when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors.
Which solution enables the company to meet these requirements?
A.  Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side.
Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL
script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation
feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the
destination for the Kinesis Data Firehose delivery stream.
B.  Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with
the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed
anomaly detection SQL script. Direct the output of KDA application to a Kinesis Data Firehose delivery
stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose
destination to an Amazon Elasticsearch Service cluster.
C.  Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side.
Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data
transformation feature to flatten the JSON file before sending it to an Amazon S3 bucket. Load the S3
data into an Amazon Redshift cluster.
D.  Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with
the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis
Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to
directly push data into it.
Answer: A
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