What Refined DAS-C01 Testing Engine Is

NEW QUESTION 1
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.

Answer: C

NEW QUESTION 2
A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s data. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables.
Which distribution style should the company use for the two tables to achieve optimal query performance?

• A. An EVEN distribution style for both tables
• B. A KEY distribution style for both tables
• C. An ALL distribution style for the product table and an EVEN distribution style for the transactions table
• D. An EVEN distribution style for the product table and an KEY distribution style for the transactions table

Answer: B

NEW QUESTION 3
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 scrip
• B. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON fil
• C. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
• D. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Jav
• E. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL scrip
• F. 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.
• G. 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 bucke
• H. Load the S3 data into an Amazon Redshift cluster.
• I. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API withthe AWS SDK for Jav
• J. 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: B

Explanation:
https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html
The KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly.

NEW QUESTION 4
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 dat
• B. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
• C. 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.
• D. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor dat
• E. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
• F. Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert message
• G. Set up AWS Application Auto Scaling on bot
• H. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message strea
• I. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.

Answer: D

NEW QUESTION 5
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 metadat
• B. Use Amazon S3 Select to retrieve the files based on the applicant name and application date.
• C. 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.
• D. Store the metadata and the Amazon S3 location of the image file in an Amazon Redshift tabl
• E. Allow the data analysts to run ad-hoc queries on the table.
• F. 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 Catalo
• G. Allow data analysts to use Amazon Athena to submit custom queries.

Answer: B

Explanation:
https://aws.amazon.com/blogs/machine-learning/automatically-extract-text-and-structured-data-from-documents

NEW QUESTION 6
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: ADE

NEW QUESTION 7
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 log
• B. Use Athena to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.
• C. 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.
• D. Create an AWS Lambda function to convert the logs into .csv forma
• E. Then add the function to the Kinesis Data Firehose transformation configuratio
• F. Use Amazon Redshift to perform ad-hoc analyses of the logs using SQL queries and use Amazon QuickSight to develop data visualizations.
• G. Create an Amazon EMR cluster and use Amazon S3 as the data sourc
• H. Create an Apache Spark job to perform ad-hoc analyses and use Amazon QuickSight to develop data visualizations.

Answer: A

Explanation:
https://aws.amazon.com/blogs/big-data/analyzing-aws-waf-logs-with-amazon-es-amazon-athena-and-amazon-qu

NEW QUESTION 8
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.

Answer: D

NEW QUESTION 9
A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.
Which elements must each query monitoring rule include?

• A. A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
• B. A queue name, a unique rule name, and a predicate-based stop condition
• C. A unique rule name, one to three predicates, and an action
• D. A workload name, a unique rule name, and a query runtime-based condition

Answer: C

NEW QUESTION 10
A large university has adopted a strategic goal of increasing diversity among enrolled students. The data analytics team is creating a dashboard with data visualizations to enable stakeholders to view historical trends. All access must be authenticated using Microsoft Active Directory. All data in transit and at rest must be encrypted.
Which solution meets these requirements?

• A. Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0. and the default encryption settings.
• B. Amazon QuickSight Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
• C. Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory.Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
• D. Amazon QuickSight Enterprise edition using AD Connector to authenticate using Active Directory.Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.

Answer: D

NEW QUESTION 11
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.

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/loading-data-files-using-manifest.html "You can use a manifest to ensure that the COPY command loads all of the required files, and only the required files, for a data load"

NEW QUESTION 12
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 tabl
• B. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
• C. Load the previously inserted data into a MySQL database in the AWS Glue jo
• D. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
• E. Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
• F. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

Answer: A

Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/sql-commands-redshift-glue-job/ See the section Merge an Amazon Redshift table in AWS Glue (upsert)

NEW QUESTION 13
A telecommunications company is looking for an anomaly-detection solution to identify fraudulent calls. The company currently uses Amazon Kinesis to stream voice call records in a JSON format from its on-premises database to Amazon S3. The existing dataset contains voice call records with 200 columns. To detect fraudulent calls, the solution would need to look at 5 of these columns only.
The company is interested in a cost-effective solution using AWS that requires minimal effort and experience in anomaly-detection algorithms.
Which solution meets these requirements?

• A. Use an AWS Glue job to transform the data from JSON to Apache Parque
• B. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalo
• C. Use Amazon Athena to create a table with a subset of column
• D. Use Amazon QuickSight to visualize the data and then use Amazon QuickSight machine learning-powered anomaly detection.
• E. Use Kinesis Data Firehose to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls and store the output in Amazon RD
• F. Use Amazon Athena to build a dataset and Amazon QuickSight to visualize the results.
• G. Use an AWS Glue job to transform the data from JSON to Apache Parque
• H. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalo
• I. Use Amazon SageMaker to build an anomaly detection model that can detect fraudulent calls by ingesting data from Amazon S3.
• J. Use Kinesis Data Analytics to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all call
• K. Connect Amazon QuickSight to Kinesis Data Analytics to visualize the anomaly scores.

Answer: A

NEW QUESTION 14
A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

• A. Enable concurrency scaling in the workload management (WLM) queue.
• B. Add more nodes using the AWS Management Console during peak hour
• C. Set the distribution style to ALL.
• D. Use elastic resize to quickly add nodes during peak time
• E. Remove the nodes when they are not needed.
• F. Use a snapshot, restore, and resize operatio
• G. Switch to the new target cluster.

Answer: A

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/cm-c-implementing-workload-management.html

NEW QUESTION 15
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.
Which solution meets these requirements?

• A. Publish data to one Kinesis data strea
• B. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SN
• C. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• D. Publish data to one Kinesis data strea
• E. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SN
• F. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• G. Publish data to two Kinesis data stream
• H. 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 SN
• I. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• J. Publish data to two Kinesis data stream
• K. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SN
• L. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

Answer: B

NEW QUESTION 16
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 Region
• C. Once the data is crawled, run Athena queries in us-west-2.
• D. 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.
• E. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2.Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

Answer: B

NEW QUESTION 17
A company is planning to do a proof of concept for a machine learning (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 cluste
• B. Store the curated data in Amazon S3 for ML processing.
• C. Create custom ETL jobs on-premises to curate the dat
• D. Use AWS DMS to ingest data into Amazon S3 for ML processing.
• E. Ingest data into Amazon S3 using AWS DM
• F. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.
• G. Take a full backup of the data store and ship the backup files using AWS Snowbal
• H. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

Answer: C

NEW QUESTION 18
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 Classi
• B. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management.
• C. Create a VPC and establish a VPN connection between the VPC and the on-premises networ
• D. Create an HSM connection and client certificate for the on-premises HS
• E. Launch a cluster in the VPC with the option to use the on-premises HSM to store keys.
• F. Create an HSM connection and client certificate for the on-premises HS
• G. Enable HSM encryption on the existing unencrypted cluster by modifying the cluste
• H. Connect to the VPC where the Amazon Redshift cluster resides from the on-premises network using a VPN.
• I. Create a replica of the on-premises HSM in AWS CloudHS
• J. Launch a cluster in a VPC with the option to use CloudHSM to store keys.

Answer: B

NEW QUESTION 19
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 dat
• B. The Lambda function will consume the data and process it to identify potential playback issue
• C. Persist the raw data to Amazon S3.
• D. Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consume
• E. The application will consume the data and process it to identify potential playback issue
• F. Persist the raw data to Amazon DynamoDB.
• G. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to proces
• H. The Lambda function will consume the data and process it to identify potential playback issue
• I. Persist the raw data to Amazon DynamoDB.
• J. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consume
• K. The application will consume the data and process it to identify potential playback issue
• L. Persist the raw data to Amazon S3.

Answer: D

Explanation:
https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/

NEW QUESTION 20
An IoT company wants to release a new device that will collect data to track sleep overnight on an intelligent mattress. Sensors will send data that will be uploaded to an Amazon S3 bucket. About 2 MB of data is generated each night for each bed. Data must be processed and summarized for each user, and the results need to be available as soon as possible. Part of the process consists of time windowing and other functions. Based on tests with a Python script, every run will require about 1 GB of memory and will complete within a couple of minutes.
Which solution will run the script in the MOST cost-effective way?

• A. AWS Lambda with a Python script
• B. AWS Glue with a Scala job
• C. Amazon EMR with an Apache Spark script
• D. AWS Glue with a PySpark job

Answer: A

NEW QUESTION 21
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