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DAS-C01 - AWS Certified Data Analytics - Specialty
For those who work in the field of data analytics, the AWS Certified Data Analytics - Specialty (DAS-C01) exam is designed. The test verifies a candidate's thorough knowledge of how to develop, create, secure, and manage analytics systems that deliver data insight using AWS services.
Main features to validates in DAS-C01 Exam
- Explain how AWS data analytics services fit into the data lifecycle of collection, storage, processing, and visualization.
- Define AWS data analytics services and comprehend how they connect with one another.
How much experience is required to achieve this certificate?
For achieving AWS Certified Data Analytics certificate a candidate must acquire minimum 7 years of experience. In which 5 years with data analyst technologies and 2 years hands on working experience.
Domain for AWS Certified Data Analytics DAS-C01 Exam
DAS-C01 - AWS Certified Data Analytics – Specialty exam formatting
- Time allowed: 1-2 hours
- Total questions: 65 questions
- Type of questions: two type of questions
- Structure of questions: multiple choice and multi response
- Cost of exam: $200-300 USD
- Language: English, Korean, Japanese
- Type of exam: VUE persona
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A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket The reports are delivered to the S3 bucket in the following format <examp/e-reporT-prefix>/<examp/e-report-rtame>/yyyymmdd-yyyymmdd/<examp/e-reportname> parquet An AWS Glue crawler crawls the S3 bucket and populates an AWS Glue Data Catalog with a table Business analysts use Amazon Athena to query the table and create monthly summary reports for the AWS accounts The business analysts are experiencing slow queries because of the accumulation of reports from the last 5 years The business analysts want the operations team to make changes to improve query performance Which action should the operations team take to meet these requirements?
A. Change the file format to csv.zip.
B. Partition the data by date and account ID
C. Partition the data by month and account ID
D. Partition the data by account ID, year, and month
ANSWER : B
A company stores Apache Parquet-formatted files in Amazon S3 The company uses an AWS Glue Data Catalog to store the table metadata and Amazon Athena to query and analyze the data The tables have a large number of partitions The queries are only run on small subsets of data in the table A data analyst adds new time partitions into the table asnew data arrives The data analyst has been asked to reduce the query runtime Which solution will provide the MOST reduction in the query runtime?
A. Convert the Parquet files to the csv file format..Then attempt to query the data again
B. Convert the Parquet files to the Apache ORC file format. Then attempt to query the data again
C. Use partition projection to speed up the processing of the partitioned table
D. Add more partitions to be used over the table. Then filter over two partitions and put all columns in the WHERE clause
ANSWER : C
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 TBof data stored on an Hadoop Distributed File System (HDFS). The company wants a costeffective 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. Configure the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
B. Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view. Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.
C. 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 Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
D. 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 nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
ANSWER : D
A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications mobile apps and operational databases The company has migrated some of these sources to AWS and now wants to migrate the Flink application. The company must ensure that data that resides in databases within the VPC does not traverse the internet The application must be able to process all the data that comes from the company's AWS solution, on-premises resources and the public internet Which solution will meet these requirements with the LEAST operational overhead?
A. Implement Flink on Amazon EC2 within the company's VPC Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the VPC to collect data that comes from applications and databases within the VPC Use Amazon Kinesis Data Streams to collect data that comes from the public internet Configure Flink to have sources from Kinesis Data Streams Amazon MSK and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
B. Implement Flink on Amazon EC2 within the company's VPC Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC andthe public internet Configure Flink to have sources from Kinesis Data Streams and any onpremises Kafka clusters by using AWS Client VPN or AWS Direct Connect
C. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink jar file Use Amazon Kinesis Data Streams to collect data that comes from applications anddatabases within the VPC and the public internet Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
D. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink jar file Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the company's VPC to collect data that comes from applications and databases within the VPC Use Amazon Kinesis Data Streams to collect data that comes from the public internet Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams. Amazon MSK and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect
ANSWER : D