[Mar-2024] DAS-C01 PDF Dumps Are Helpful To produce Your Dreams Correct QA's
New DAS-C01 exam Free Sample Questions to Practice
Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam is a certification exam designed for professionals who are looking to validate their technical skills in designing, building, and maintaining big data solutions using Amazon Web Services (AWS). AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification exam covers a wide range of topics related to big data, including data visualization, data processing, data storage, and data analysis. By passing DAS-C01 exam, professionals can demonstrate their expertise in using AWS services for big data analytics and improve their career prospects in the field of data analytics.
The AWS Certified Data Analytics – Specialty certification exam covers a range of topics including data collection, processing, analysis, visualization, and interpretation. DAS-C01 exam also covers AWS services such as Amazon Kinesis, Amazon Redshift, Amazon Athena, and Amazon QuickSight. Candidates for the exam should have a deep understanding of AWS data analytics services and how to use them to build effective solutions.
Topics of AWS Certified Data Analytics - Specialty (DAS-C01) Professional Exam
Candidates must know the test themes prior to the start of their exam preparations, as it will help them in acing the exam. AMAZON DAS C01 exam dumps pdf will incorporate the accompanying themes:
- Storage and Data Management
- Collection
- Processing
- Analysis and Visualization
- Security
NEW QUESTION # 24
A company has several Amazon EC2 instances sitting behind an Application Load Balancer (ALB) The company wants its IT Infrastructure team to analyze the IP addresses coming into the company's ALB The ALB is configured to store access logs in Amazon S3 The access logs create about 1 TB of data each day, and access to the data will be infrequent The company needs a solution that is scalable, cost-effective and has minimal maintenance requirements Which solution meets these requirements?
- A. Use Amazon EMR and Apache Hive to query the S3 data
- B. Use Amazon Athena to query the S3 data
- C. Copy the data into Amazon Redshift and query the data
- D. Use Amazon Redshift Spectrum to query the S3 data
Answer: D
NEW QUESTION # 25
An insurance company has raw data in JSON format that is sent without a predefined schedule through an Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to provide access to the most up-to-date data.
Which solution meets these requirements?
- A. Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.
- B. Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.
- C. Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event notification on the S3 bucket.
- D. Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler every hour.
Answer: C
Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html "you can use a wildcard (for example, s3:ObjectCreated:*) to request notification when an object is created regardless of the API used" "AWS Lambda can run custom code in response to Amazon S3 bucket events. You upload your custom code to AWS Lambda and create what is called a Lambda function. When Amazon S3 detects an event of a specific type (for example, an object created event), it can publish the event to AWS Lambda and invoke your function in Lambda. In response, AWS Lambda runs your function."
NEW QUESTION # 26
A company is sending historical datasets to Amazon S3 for storage. A data engineer at the company wants to make these datasets available for analysis using Amazon Athen a. The engineer also wants to encrypt the Athena query results in an S3 results location by using AWS solutions for encryption. The requirements for encrypting the query results are as follows:
Use custom keys for encryption of the primary dataset query results.
Use generic encryption for all other query results.
Provide an audit trail for the primary dataset queries that shows when the keys were used and by whom.
Which solution meets these requirements?
- A. Use client-side encryption with AWS Key Management Service (AWS KMS) customer managed keys for the primary dataset. Use S3 client-side encryption with client-side keys for the other datasets.
- B. Use server-side encryption with customer-provided encryption keys (SSE-C) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
- C. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primary dataset. Use SSE-S3 for the other datasets.
- D. Use server-side encryption with AWS KMS managed customer master keys (SSE-KMS CMKs) for the primary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the other datasets.
Answer: C
NEW QUESTION # 27
A marketing company is storing its campaign response data in Amazon S3. A consistent set of sources has generated the data for each campaign. The data is saved into Amazon S3 as .csv files. A business analyst will use Amazon Athena to analyze each campaign's dat a. The company needs the cost of ongoing data analysis with Athena to be minimized.
Which combination of actions should a data analytics specialist take to meet these requirements? (Choose two.)
- A. Convert the .csv files to Apache Avro.
- B. Compress the .csv files.
- C. Convert the .csv files to Apache Parquet.
- D. Partition the data by source.
- E. Partition the data by campaign.
Answer: C,E
Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
NEW QUESTION # 28
A manufacturing company has many loT devices in different facilities across the world The company is using Amazon Kinesis Data Streams to collect the data from the devices The company's operations team has started to observe many WnteThroughputExceeded exceptions The operations team determines that the reason is the number of records that are being written to certain shards The data contains device ID capture date measurement type, measurement value and facility ID The facility ID is used as the partition key Which action will resolve this issue?
- A. Change the partition key from facility ID to capture date
- B. Change the partition key from facility ID to a randomly generated key
- C. Archive the data on the producers' side
- D. Increase the number of shards
Answer: D
NEW QUESTION # 29
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. Use a snapshot, restore, and resize operation. Switch to the new target cluster.
- B. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
- C. Enable concurrency scaling in the workload management (WLM) queue.
- D. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
Answer: C
NEW QUESTION # 30
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 table. 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 exists. Change the SQL query in the application to include the new field in the SELECT statement.
- B. Store the contents of the mapping file in an Amazon DynamoDB table. Change the Kinesis Data Analytics 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 exists. Forward the record from the Lambda function to the original application destination.
- C. Store the mapping file in an Amazon S3 bucket and configure it as a reference data source for the Kinesis Data Analytics application. 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.
- 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 application. 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.
Answer: C
NEW QUESTION # 31
A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?
- A. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.
- B. Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.
- C. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
- D. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script.
Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.
Answer: B
NEW QUESTION # 32
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 Streams. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status codes. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehouse.
- B. 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 previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.
- C. Ingest the data stream with Amazon Kinesis Data Streams. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.
- D. 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 codes. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.
Answer: A
NEW QUESTION # 33
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 Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
- B. Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory.
Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS. - C. Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0. and the default encryption settings.
- 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 # 34
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 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.
- B. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. 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 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: C
Explanation:
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 # 35
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 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.
- B. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.
- C. 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.
- D. 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: C
NEW QUESTION # 36
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?
- A. Use AWS Glue to convert the files from .csv to a single large Apache ORC file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
- B. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet files. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.
- C. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet file. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
- D. Use Amazon EMR to convert each .csv file to Apache Avro. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
Answer: B
NEW QUESTION # 37
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. Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore
- B. Amazon Athena for querying data in Amazon S3 using JDBC drivers
- C. AWS Glue Data Catalog for metadata management
- D. Amazon EMR with Apache Hive for JDBC clients
- E. AWS Glue for Scala-based ETL
- F. Amazon EMR with Apache Spark for ETL
Answer: A,B,F
NEW QUESTION # 38
A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.
Which approach is the MOST efficient way to meet these requirements?
- A. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
- B. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.
- C. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
- D. Use Amazon Kinesis Data Streams to collect all the data from toll stations. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.
Answer: B
NEW QUESTION # 39
A software company hosts an application on AWS, and new features are released weekly. As part of the application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance to ensure that the application is working as expected after each deployment. The collection and analysis solution should be highly available with the ability to display new information with minimal delays.
Which method should the company use to collect and analyze the logs?
- A. Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.
- B. Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.
- C. Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon S3, and use Amazon Athena for fast, interactive log analytics.
- D. Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using Amazon QuickSight.
Answer: B
NEW QUESTION # 40
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. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.
- C. Set up an Amazon Redshift VPC security group for Australia and the US.
- D. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.
Answer: B
NEW QUESTION # 41
A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)
- A. Convert the log files to Apace Avro format.
- B. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
- C. Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.
- D. Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.
- E. Convert the log files to Apache Parquet format.
- F. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
Answer: B,C,E
Explanation:
Section: (none)
Explanation
NEW QUESTION # 42
A US-based sneaker retail company launched its global website. All the transaction data is stored in Amazon RDS and curated historic transaction data is stored in Amazon Redshift in the us-east-1 Region. The business intelligence (BI) team wants to enhance the user experience by providing a dashboard for sneaker trends.
The BI team decides to use Amazon QuickSight to render the website dashboards. During development, a team in Japan provisioned Amazon QuickSight in ap-northeast-1. The team is having difficulty connecting Amazon QuickSight from ap-northeast-1 to Amazon Redshift in us-east-1.
Which solution will solve this issue and meet the requirements?
- A. Create a new security group for Amazon Redshift in us-east-1 with an inbound rule authorizing access from the appropriate IP address range for the Amazon QuickSight servers in ap-northeast-1.
- B. Create an Amazon Redshift endpoint connection string with Region information in the string and use this connection string in Amazon QuickSight to connect to Amazon Redshift.
- C. Create a VPC endpoint from the Amazon QuickSight VPC to the Amazon Redshift VPC so Amazon QuickSight can access data from Amazon Redshift.
- D. In the Amazon Redshift console, choose to configure cross-Region snapshots and set the destination Region as ap-northeast-1. Restore the Amazon Redshift Cluster from the snapshot and connect to Amazon QuickSight launched in ap-northeast-1.
Answer: C
NEW QUESTION # 43
A mobile gaming company wants to capture data from its gaming app and make the data available for analysis immediately. The data record size will be approximately 20 KB. The company is concerned about achieving optimal throughput from each device. Additionally, the company wants to develop a data stream processing application with dedicated throughput for each consumer.
Which solution would achieve this goal?
- A. Have the app call the PutRecordBatch API to send data to Amazon Kinesis Data Firehose. Submit a support case to enable dedicated throughput on the account.
- B. Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature while consuming the data.
- C. Have the app call the PutRecords API to send data to Amazon Kinesis Data Streams. Host the stream- processing application on Amazon EC2 with Auto Scaling.
- D. Have the app use Amazon Kinesis Producer Library (KPL) to send data to Kinesis Data Firehose. Use the enhanced fan-out feature while consuming the data.
Answer: B
NEW QUESTION # 44
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 Enterprise edition configured to perform identity federation using SAML 2.0 and the default encryption settings.
- B. Amazon QuckSight Standard edition using AD Connector to authenticate using Active Directory. Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
- C. Amazon QuickSight Enterprise edition using AD Connector to authenticate using Active Directory. Configure Amazon QuickSight to use customer-provided keys imported into AWS KMS.
- D. Amazon QuickSight Standard edition configured to perform identity federation using SAML 2.0. and the default encryption settings.
Answer: C
NEW QUESTION # 45
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