The Latest Guide To DAS-C01 Free Demo

NEW QUESTION 1
An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.
Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)

• A. Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
• B. Use an S3 bucket in the same account as Athena.
• C. Compress the objects to reduce the data transfer I/O.
• D. Use an S3 bucket in the same Region as Athena.
• E. Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
• F. Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

Answer: CDF

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/

NEW QUESTION 2
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.

Answer: BD

NEW QUESTION 3
A company has a marketing department and a finance department. The departments are storing data in Amazon S3 in their own AWS accounts in AWS Organizations. Both departments use AWS Lake Formation to catalog and secure their data. The departments have some databases and tables that share common names.
The marketing department needs to securely access some tables from the finance department. Which two steps are required for this process? (Choose two.)

• A. The finance department grants Lake Formation permissions for the tables to the external account for the marketing department.
• B. The finance department creates cross-account IAM permissions to the table for the marketing department role.
• C. The marketing department creates an IAM role that has permissions to the Lake Formation tables.

Answer: AB

Explanation:
Granting Lake Formation Permissions Creating an IAM role (AWS CLI)

NEW QUESTION 4
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 5
A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

• A. Load the data into an Amazon Redshift cluster by using the COPY comman
• B. Configure 50 author users and 1,000 reader user
• C. Use QuickSight Enterprise editio
• D. Configure an Amazon Redshift data source with a direct query option.
• E. Use QuickSight Standard editio
• F. Configure 50 author users and 1,000 reader user
• G. Configure an Athena data source with a direct query option.
• H. Use QuickSight Enterprise editio
• I. Configure 50 author users and 1,000 reader user
• J. Configure an Athena data source and import the data into SPIC
• K. Automatically refresh every 24 hours.
• L. Use QuickSight Enterprise editio
• M. Configure 1 administrator and 1,000 reader user
• N. Configure an S3 data source and import the data into SPIC
• O. Automatically refresh every 24 hours.

Answer: C

NEW QUESTION 6
A company has a data lake on AWS that ingests sources of data from multiple business units and uses Amazon Athena for queries. The storage layer is Amazon S3 using the AWS Glue Data Catalog. The company wants to make the data available to its data scientists and business analysts. However, the company first needs to manage data access for Athena based on user roles and responsibilities.
What should the company do to apply these access controls with the LEAST operational overhead?

• A. Define security policy-based rules for the users and applications by role in AWS Lake Formation.
• B. Define security policy-based rules for the users and applications by role in AWS Identity and Access Management (IAM).
• C. Define security policy-based rules for the tables and columns by role in AWS Glue.
• D. Define security policy-based rules for the tables and columns by role in AWS Identity and Access Management (IAM).

Answer: D

NEW QUESTION 7
A company hosts an on-premises PostgreSQL database that contains historical data. An internal legacy application uses the database for read-only activities. The company’s business team wants to move the data to a data lake in Amazon S3 as soon as possible and enrich the data for analytics.
The company has set up an AWS Direct Connect connection between its VPC and its on-premises network. A data analytics specialist must design a solution that achieves the business team’s goals with the least operational overhead.
Which solution meets these requirements?

• A. Upload the data from the on-premises PostgreSQL database to Amazon S3 by using a customized batch upload proces
• B. Use the AWS Glue crawler to catalog the data in Amazon S3. Use an AWS Glue job to enrich and store the result in a separate S3 bucket in Apache Parquet forma
• C. Use Amazon Athena to query the data.
• D. Create an Amazon RDS for PostgreSQL database and use AWS Database Migration Service (AWS DMS) to migrate the data into Amazon RD
• E. Use AWS Data Pipeline to copy and enrich the data from the Amazon RDS for PostgreSQL table and move the data to Amazon S3. Use Amazon Athena to querythe data.
• F. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises databas
• G. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet forma
• H. Create an Amazon Redshift cluster and use Amazon Redshift Spectrum to query the data.
• I. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises databas
• J. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet forma
• K. Use Amazon Athena to query the data.

Answer: B

NEW QUESTION 8
A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.
Which solution will accomplish this task?

• A. An AWS Glue ETL job with the FindMatches transform
• B. Amazon Kendra
• C. Amazon SageMaker Ground Truth
• D. An AWS Glue ETL job with the ResolveChoice transform

Answer: A

Explanation:
Matching Records with AWS Lake Formation FindMatches

NEW QUESTION 9
Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon S3 bucket. The EMR cluster has Kerberos enabled and is configured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?

• A. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.Create three additional IAM roles, each granting access to each team’s specific bucke
• B. Add the additional IAM roles to the cluster’s EMR role for the EC2 trust polic
• C. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• D. For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3.Create three additional IAM roles, each granting access to each team's specific bucke
• E. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM role
• F. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• G. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.Create three additional IAM roles, each granting access to each team’s specific bucke
• H. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM role
• I. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.
• J. For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3.Create three additional IAM roles, each granting access to each team's specific bucke
• K. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM role
• L. Create a security configuration mapping for the additional IAM roles to Active Directory user groups for each team.

Answer: C

NEW QUESTION 10
A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.
Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners’ dashboards?

• A. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.
• B. Create and schedule an AWS Glue Spark job to run every 5 minute
• C. The job inserts reference data into Amazon Redshift.
• D. Send reference data to Amazon Kinesis Data Stream
• E. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
• F. Send the reference data to an Amazon Kinesis Data Firehose delivery strea
• G. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

Answer: A

NEW QUESTION 11
A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala. Operational management should be limited.
Which combination of components can meet these requirements? (Choose three.)

• A. AWS Glue Data Catalog for metadata management
• B. Amazon EMR with Apache Spark for ETL
• C. AWS Glue for Scala-based ETL
• D. Amazon EMR with Apache Hive for JDBC clients
• E. Amazon Athena for querying data in Amazon S3 using JDBC drivers
• F. Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore

Answer: BEF

NEW QUESTION 12
A company has an encrypted Amazon Redshift cluster. The company recently enabled Amazon Redshift audit logs and needs to ensure that the audit logs are also encrypted at rest. The logs are retained for 1 year. The auditor queries the logs once a month.
What is the MOST cost-effective way to meet these requirements?

• A. Encrypt the Amazon S3 bucket where the logs are stored by using AWS Key Management Service (AWS KMS). Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basi
• B. Query the data as required.
• C. Disable encryption on the Amazon Redshift cluster, configure audit logging, and encrypt the Amazon Redshift cluste
• D. Use Amazon Redshift Spectrum to query the data as required.
• E. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryptio
• F. Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basi
• G. Query the data as required.
• H. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryptio
• I. Use Amazon Redshift Spectrum to query the data as required.

Answer: A

NEW QUESTION 13
A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.
Which solution will meet these requirements?

• A. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
• B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
• C. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
• D. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

Answer: A

NEW QUESTION 14
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 15
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 16
An online gaming company is using an Amazon Kinesis Data Analytics SQL application with a Kinesis data stream as its source. The source sends three non-null fields to the application: player_id, score, and us_5_digit_zip_code.
A data analyst has a .csv mapping file that maps a small number of us_5_digit_zip_code values to a territory code. The data analyst needs to include the territory code, if one exists, as an additional output of the Kinesis Data Analytics application.
How should the data analyst meet this requirement while minimizing costs?

• A. Store the contents of the mapping file in an Amazon DynamoDB tabl
• B. Preprocess the records as they arrive in the Kinesis Data Analytics application with an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exist
• C. Change the SQL query in the application to include the new field in the SELECT statement.
• D. Store the mapping file in an Amazon S3 bucket and configure the reference data column headers for the.csv file in the Kinesis Data Analytics applicatio
• E. Change the SQL query in the application to include a join to the file’s S3 Amazon Resource Name (ARN), and add the territory code field to the SELECT columns.
• F. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics applicatio
• G. Change the SQL query in the application to include a join to the reference table and add the territory code field to the SELECT columns.
• H. Store the contents of the mapping file in an Amazon DynamoDB tabl
• I. Change the Kinesis DataAnalytics application to send its output to an AWS Lambda function that fetches the mapping and supplements each record to include the territory code, if one exist
• J. Forward the record from the Lambda function to the original application destination.

Answer: C

NEW QUESTION 17
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: A

NEW QUESTION 18
A company’s data analyst needs to ensure that queries executed in Amazon Athena cannot scan more than a prescribed amount of data for cost control purposes. Queries that exceed the prescribed threshold must be canceled immediately.
What should the data analyst do to achieve this?

• A. Configure Athena to invoke an AWS Lambda function that terminates queries when the prescribed threshold is crossed.
• B. For each workgroup, set the control limit for each query to the prescribed threshold.
• C. Enforce the prescribed threshold on all Amazon S3 bucket policies
• D. For each workgroup, set the workgroup-wide data usage control limit to the prescribed threshold.

Answer: B

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/manage-queries-control-costs-with-workgroups.html

NEW QUESTION 19
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 20
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