What Best Quality DAS-C01 Simulations Is

NEW QUESTION 1
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 2
A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .c sv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements.
Which solution meets the company’s requirements?

• A. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data forma
• B. Use Athena to query the processed datase
• C. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creatio
• D. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
• E. Use an AWS Glue ETL job to partition and convert the data into a row-based data forma
• F. Use Athena to query the processed datase
• G. Configure a lifecycle policy to move the data into the Amazon S3 Standard- Infrequent Access (S3 Standard-IA) storage class 5 years after object creatio
• H. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
• I. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data forma
• J. Use Athena to query the processed datase
• K. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accesse
• L. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier forlong-term archival 7 days after the last date the object was accessed.
• M. Use an AWS Glue ETL job to partition and convert the data into a row-based data forma
• N. Use Athena to query the processed datase
• O. Configure a lifecycle policy to move the data into the Amazon S3 Standard- Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accesse
• P. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.

Answer: A

NEW QUESTION 3
A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.
What should the data analyst do to ensure the appropriate data security is in place?

• A. Place the data sources for Australia and the US into separate SPICE capacity pools.
• B. Set up an Amazon Redshift VPC security group for Australia and the US.
• C. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.
• D. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

Answer: D

NEW QUESTION 4
A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most
cost-effective solution.
Which approach meets these requirements for optimizing and querying the log data?

• A. Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and da
• B. Use AWS Glue crawlers to detect new partition
• C. Use Amazon Athena to query data.
• D. Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and da
• E. Use Apache Presto to query the optimized format.
• F. Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and da
• G. Use Amazon Redshift Spectrum to query the data.
• H. Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and da
• I. Use AWS Glue crawlers to detect new partition
• J. Use Amazon Athena to querydata.

Answer: C

NEW QUESTION 5
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 bucke
• B. Set up S3 bucket policies to provide marketing employees with appropriate data access under compliance control
• C. Delete the bucket policies after the project.
• D. Load tabular data from Amazon S3 to an Amazon EMR cluster using s3DistC
• E. Implement a customHadoop-based row-level security solution on the Hadoop Distributed File System (HDFS) to provide marketing employees with appropriate data access under compliance control
• F. Terminate the EMR cluster after the project.
• G. Load tabular data from Amazon S3 to Amazon Redshift with the COPY comman
• H. Use the built-in row- level security feature in Amazon Redshift to provide marketing employees with appropriate data access under compliance control
• I. Delete the Amazon Redshift tables after the project.
• J. Load tabular data from Amazon S3 to Amazon QuickSight Enterprise edition by directly importing it as a data sourc
• K. Use the built-in row-level security feature in Amazon QuickSight to provide marketing employees with appropriate data access under compliance control
• L. Delete Amazon QuickSight data sources after the project is complete.

Answer: C

NEW QUESTION 6
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 near-real-time data.
Which visualization solution will meet these requirements?

• A. Select Amazon Elasticsearch Service (Amazon ES) as the endpoint for Kinesis Data Firehos
• B. Set up a Kibana dashboard using the data in Amazon ES with the desired analyses and visualizations.
• C. Select Amazon S3 as the endpoint for Kinesis Data Firehos
• D. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.
• E. Select Amazon Redshift as the endpoint for Kinesis Data Firehos
• F. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.
• G. Select Amazon S3 as the endpoint for Kinesis Data Firehos
• H. Use AWS Glue to catalog the data and Amazon Athena to query i
• I. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.

Answer: A

NEW QUESTION 7
A company wants to collect and process events data from different departments in near-real time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?

• A. Use Amazon Kinesis Data Stream
• B. Configure a stream for the raw dat
• C. Use a Kinesis Agent to write data to the strea
• D. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.
• E. Use Amazon Kinesis Data Firehos
• F. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansin
• G. Use a Kinesis Agent to write data to the delivery strea
• H. Configure Kinesis Data Firehose to deliver the data to Amazon S3.
• I. Use Amazon Managed Streaming for Apache Kafk
• J. Configure a topic for the raw dat
• K. Use a Kafka producer to write data to the topi
• L. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.
• M. Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.

Answer: B

NEW QUESTION 8
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 the 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.

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c_loading-data-best-practices.html

NEW QUESTION 9
A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

• A. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM user
• B. and apply the S3 bucket policy to the S3 bucket.
• C. Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.
• D. Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.
• E. Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

Answer: B

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/user-created-workgroups.html
Amazon Athena Workgroups - A new resource type that can be used to separate query execution and query history between Users, Teams, or Applications running under the same AWS account https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/

NEW QUESTION 10
A company’s marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
The data size is approximately 32 TB uncompressed.
There is a low volume of single-row inserts each day.
There is a high volume of aggregation queries each day.
Multiple complex joins are performed.
The queries typically involve a small subset of the columns in a table. Which storage service will provide the MOST performant solution?

• A. Amazon Aurora MySQL
• B. Amazon Redshift
• C. Amazon Neptune
• D. Amazon Elasticsearch

Answer: B

NEW QUESTION 11
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.
Which solution meets these requirements?

• A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function.Perform the join with AWS Glue ETL scripts.
• B. Export the call center data from Amazon Redshift using a Python shell in AWS Glu
• C. Perform the join with AWS Glue ETL scripts.
• D. Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.
• E. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoo
• F. Perform the join with Apache Hive.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/c-spectrum-external-tables.html

NEW QUESTION 12
A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL.
Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams.
Which solution meets these requirements?

• A. Increase the query priority to HIGHEST for the data analysis queue.
• B. Configure the data analysis queue to enable concurrency scaling.
• C. Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.
• D. Use workload management query queue hopping to route the query to the next matching queue.

Answer: D

NEW QUESTION 13
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 sourc
• B. Use heat maps as the visual type.
• C. Use Amazon QuickSight with Amazon S3 as the data sourc
• D. Use heat maps as the visual type.
• E. Use Amazon QuickSight with Amazon Athena as the data sourc
• F. Use pivot tables as the visual type.
• G. Use Amazon QuickSight with Amazon S3 as the data sourc
• H. Use pivot tables as the visual type.

Answer: A

NEW QUESTION 14
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 dat
• B. Then use Amazon Redshift for the additional analysis.
• C. Keep the data from the last 90 days in Amazon Redshif
• D. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by dat
• E. Then use Amazon Redshift Spectrum for the additional analysis.
• F. Keep the data from the last 90 days in Amazon Redshif
• G. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by dat
• H. Then provision a persistent Amazon EMR cluster and use Apache Presto for the additional analysis.
• I. 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 cluste
• J. Then use Amazon Redshift for the additional analysis.

Answer: B

NEW QUESTION 15
A company uses the Amazon Kinesis SDK to write data to Kinesis Data Streams. Compliance requirements state that the data must be encrypted at rest using a key that can be rotated. The company wants to meet this encryption requirement with minimal coding effort.
How can these requirements be met?

• A. Create a customer master key (CMK) in AWS KM
• B. Assign the CMK an alia
• C. Use the AWS Encryption SDK, providing it with the key alias to encrypt and decrypt the data.
• D. Create a customer master key (CMK) in AWS KM
• E. Assign the CMK an alia
• F. Enable server-side encryption on the Kinesis data stream using the CMK alias as the KMS master key.
• G. Create a customer master key (CMK) in AWS KM
• H. Create an AWS Lambda function to encrypt and decrypt the dat
• I. Set the KMS key ID in the function’s environment variables.
• J. Enable server-side encryption on the Kinesis data stream using the default KMS key for Kinesis Data Streams.

Answer: B

NEW QUESTION 16
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 17
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.Configurethe EMR cluster with multiple master node
• B. Schedule automated snapshots using AmazonEventBridge.
• C. Store the data on an EMR File System (EMRFS) instead of HDF
• D. Enable EMRFS consistent view.Create an EMR HBase cluster with multiple master node
• E. Point the HBase root directory to an Amazon S3 bucket.
• F. 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 Zone
• G. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• H. 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 node
• I. Create a secondary EMR HBase read- replica cluster in a separate Availability Zon
• J. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

Answer: D

NEW QUESTION 18
A marketing company has data in Salesforce, MySQL, and Amazon S3. The company wants to use data from these three locations and create mobile dashboards for its users. The company is unsure how it should create the dashboards and needs a solution with the least possible customization and coding.
Which solution meets these requirements?

• A. Use Amazon Athena federated queries to join the data source
• B. Use Amazon QuickSight to generate the mobile dashboards.
• C. Use AWS Lake Formation to migrate the data sources into Amazon S3. Use Amazon QuickSight to generate the mobile dashboards.
• D. Use Amazon Redshift federated queries to join the data source
• E. Use Amazon QuickSight to generate the mobile dashboards.
• F. Use Amazon QuickSight to connect to the data sources and generate the mobile dashboards.

Answer: C

NEW QUESTION 19
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