Amazon-Web-Services DAS-C01 Free Practice Questions

NEW QUESTION 1
A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis Data Streams to collect the data streams from smart meters. The consumer application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.
The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.
Which solution meets these requirements?

• A. Use enhanced fan-out in Kinesis Data Streams.
• B. Increase the number of shards for the Kinesis data stream.
• C. Reduce the propagation delay by overriding the KCL default settings.
• D. Develop consumers by using Amazon Kinesis Data Firehose.

Answer: C

Explanation:
The KCL defaults are set to follow the best practice of polling every 1 second. This default results in average propagation delays that are typically below 1 second.

NEW QUESTION 2
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 3
A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:
The operations team reports are run hourly for the current month’s data.
The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories.
The sales team also wants to view the data as soon as it reaches the reporting backend.
The finance team’s reports are run daily for last month’s data and once a month for the last 24 months of data.
Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost-effective as possible.
Which solution meets the company’s requirements?

• A. Store the last 24 months of data in Amazon Redshif
• B. Configure Amazon QuickSight with Amazon Redshift as the data source.
• C. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectru
• D. Configure Amazon QuickSight with Amazon Redshift as the data source.
• E. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum.Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.
• F. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long- running Amazon EMR with Apache Spark cluster to query the data as neede
• G. Configure Amazon QuickSight with Amazon EMR as the data source.

Answer: B

NEW QUESTION 4
A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?

• A. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.
• B. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.
• C. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.
• D. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

Answer: A

Explanation:
https://docs.aws.amazon.com/quicksight/latest/user/directory-integration.html

NEW QUESTION 5
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 6
A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient’s protected health information (PHI) from the streaming data and store the data in durable storage.
Which solution meets these requirements with the least operational overhead?

• A. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PH
• B. Write the data in Amazon S3.
• C. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
• D. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
• E. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

Answer: D

Explanation:
https://aws.amazon.com/blogs/big-data/persist-streaming-data-to-amazon-s3-using-amazon-kinesis-firehose-and

NEW QUESTION 7
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)

• A. Increase the Kinesis Data Streams retention period to reduce throttling.
• B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
• C. Increase the number of shards in the stream using the UpdateShardCount API.
• D. Choose partition keys in a way that results in a uniform record distribution across shards.
• E. Customize the application code to include retry logic to improve performance.

Answer: CD

Explanation:
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/

NEW QUESTION 8
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 9
A company has an application that ingests streaming data. The company needs to analyze this stream over a 5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.
Which approach would enable the desired outcome while keeping data persistence costs low?

• A. Ingest the data stream with Amazon Kinesis Data Stream
• B. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF mode
• C. Persist the source and results as a time series to Amazon DynamoDB.
• D. Ingest the data stream with Amazon Kinesis Data Stream
• E. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status code
• F. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehouse.
• G. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute or 1 MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previouslytrained RCF mode
• H. Persist the source and results as a time series to Amazon DynamoDB.
• I. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or 1 MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a 1-minute window using the RCF function and summarize the count of status code
• J. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Answer: B

NEW QUESTION 10
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 11
A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.
Which solution achieves these required access patterns to minimize costs and administrative tasks?

• A. Consolidate all AWS accounts into one accoun
• B. Create different S3 buckets for each department and move all the data from every account to the central data lake accoun
• C. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.
• D. Keep the account structure and the individual AWS Glue catalogs on each accoun
• E. Add a central data lake account and use AWS Glue to catalog data from various account
• F. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalo
• G. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazon S3.
• H. Set up an individual AWS account for the central data lak
• I. Use AWS Lake Formation to catalog the cross- account location
• J. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked rol
• K. Use Lake Formation permissions to addfine-grained access controls to allow senior analysts to view specific tables and columns.
• L. Set up an individual AWS account for the central data lake and configure a central S3 bucke
• M. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucke
• N. On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked rol
• O. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific tables and columns.

Answer: C

Explanation:
Lake Formation provides secure and granular access to data through a new grant/revoke permissions model that augments AWS Identity and Access Management (IAM) policies. Analysts and data scientists can use the full portfolio of AWS analytics and machine learning services, such as Amazon Athena, to access the data. The configured Lake Formation security policies help ensure that users can access only the data that they are authorized to access. Source : https://docs.aws.amazon.com/lake-formation/latest/dg/how-it-works.html

NEW QUESTION 12
A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is
generated daily as .csv files that are stored in Amazon S3. The company’s analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)

• A. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connecto
• B. Run the query from MySQL Workbench instead of Athena directly.
• C. Use Athena to extract the data and store it in Apache Parquet format on a daily basi
• D. Query the extracted data.
• E. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted file
• F. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
• G. Run a daily AWS Glue ETL job to compress the data files by using the .gzip forma
• H. Query the compressed data.
• I. Run a daily AWS Glue ETL job to compress the data files by using the .lzo forma
• J. Query the compressed data.

Answer: BC

NEW QUESTION 13
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.
Which solution meets these requirements?

• A. Use Amazon Aurora as the data catalo
• B. Create AWS Lambda functions that will connect and gather themetadata information from multiple sources and update the data catalog in Auror
• C. Schedule the Lambda functions periodically.
• D. Use the AWS Glue Data Catalog as the central metadata repositor
• E. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata change
• F. Schedule the crawlers periodically to update the metadata catalog.
• G. Use Amazon DynamoDB as the data catalo
• H. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalo
• I. Schedule the Lambda functions periodically.
• J. Use the AWS Glue Data Catalog as the central metadata repositor
• K. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalo
• L. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.

Answer: D

NEW QUESTION 14
A manufacturing company uses Amazon Connect to manage its contact center and Salesforce to manage its customer relationship management (CRM) data. The data engineering team must build a pipeline to ingest data from the contact center and CRM system into a data lake that is built on Amazon S3.
What is the MOST efficient way to collect data in the data lake with the LEAST operational overhead?

• A. Use Amazon Kinesis Data Streams to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.
• B. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon Kinesis Data Streams to ingest Salesforce data.
• C. Use Amazon Kinesis Data Firehose to ingest Amazon Connect data and Amazon AppFlow to ingest Salesforce data.
• D. Use Amazon AppFlow to ingest Amazon Connect data and Amazon Kinesis Data Firehose to ingest Salesforce data.

Answer: B

NEW QUESTION 15
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 16
An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?

• A. Enable Amazon Redshift Enhanced VPC Routin
• B. Enable VPC Flow Logs to monitor traffic.
• C. Allow access to the Amazon Redshift database using AWS IAM onl
• D. Log access using AWS CloudTrail.
• E. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
• F. Enable and download audit reports from AWS Artifact.

Answer: C

NEW QUESTION 17
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 18
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