Amazon DAS-C01 dumps

Amazon DAS-C01 Exam Dumps

AWS Certified Data Analytics - Specialty
673 Reviews

Exam Code DAS-C01
Exam Name AWS Certified Data Analytics - Specialty
Questions 157 Questions Answers With Explanation
Update Date February 12,2024
Price Was : $81 Today : $45 Was : $99 Today : $55 Was : $117 Today : $65

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Amazon DAS-C01 Sample Questions

Question # 1

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 



Question # 2

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables. A trips fact table for information on completed rides. A drivers dimension table for driver profiles. A customers fact table holding customer profile information. The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes. What table design provides optimal query performance?

A. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables. 
B. Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table. 
C. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table. 
D. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables. 



Question # 3

An analytics software as a service (SaaS) provider wants to offer its customers business intelligence <BI) reporting capabilities that are self-service The provider is using AmazonQuickSight to build these reports The data for the reports resides in a multi-tenant database, but each customer should only be able to access their own data The provider wants to give customers two user role options • Read-only users for individuals who only need to view dashboards • Power users for individuals who are allowed to create and share new dashboards withother users Which QuickSight feature allows the provider to meet these requirements'?

A. Embedded dashboards 
B. Table calculations 
C. Isolated namespaces 
D. SPICE 



Question # 4

A software company wants to use instrumentation data to detect and resolve errors to improve application recovery time. The company requires API usage anomalies, like error rate and response time spikes, to be detected in near-real time (NRT) The company also requires that data analysts have access to dashboards for log analysis in NRT Which solution meets these requirements'? 

A. Use Amazon Kinesis Data Firehose as the data transport layer for logging data Use Amazon Kinesis Data Analytics to uncover the NRT API usage anomalies Use Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use OpenSearch Dashboards (Kibana) in Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboards. 
B. Use Amazon Kinesis Data Analytics as the data transport layer for logging data. Use Amazon Kinesis Data Streams to uncover NRT monitoring metrics. Use Amazon Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use Amazon QuickSight for the dashboards 
C. Use Amazon Kinesis Data Analytics as the data transport layer for logging data and to uncover NRT monitoring metrics Use Amazon Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use OpenSearch Dashboards (Kibana) in Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboards 
D. Use Amazon Kinesis Data Firehose as the data transport layer for logging data Use Amazon Kinesis Data Analytics to uncover NRT monitoring metrics Use Amazon Kinesis Data Streams to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use Amazon QuickSight for the dashboards.



Question # 5

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync Which solution will simplify permissions management with minimal development effort?

A. Set AWS Identity and Access Management (1AM) permissions tor AWS Glue 
B. Use AWS Lake Formation permissions 
C. Manage AWS Glue and S3 permissions by using bucket policies 
D. Use Amazon Cognito user pools. 



Question # 6

A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3 Each day the data is stored in an individual csv file in an S3 bucket This is an example of the naming structure 20210707_datacsv 20210708_datacsv To allow for data querying in QuickSight through Amazon Athena the specialist used an AWS Glue crawler to create a table with the path "s3 //powertransformer/20210707_data csv" However when the data is queried, it returns zero rows How can this issue be resolved?

A. Modify the IAM policy for the AWS Glue crawler to access Amazon S3. 
B. Ingest the files again. 
C. Store the files in Apache Parquet format. 
D. Update the table path to "s3://powertransformer/". 



Question # 7

A company using Amazon QuickSight Enterprise edition has thousands of dashboards analyses and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management. Which solution should the company implement to simplify permissions management?

A. Use QuickSight folders to organize dashboards, analyses, and datasets Assign individual users permissions to these folders 
B. Use QuickSight folders to organize dashboards analyses, and datasets Assign group permissions by using these folders. 
C. Use AWS 1AM resource-based policies to assign group permissions to QuickSight items 
D. Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions 



Question # 8

A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly Which solution will meet these requirements with the LEAST operational overhead? 

A. Create an Amazon EMR cluster to process and analyze data in the databases Connect to the Apache Zeppelin notebook, and use the FindMatches transform to find duplicate records in the data. 
B. Create an AWS Glue crawler to crawl the databases. Use the FindMatches transform to find duplicate records in the data Evaluate and tune the transform by evaluating performance and results of finding matches 
C. Create an AWS Glue crawler to crawl the data in the databases Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data 
D. Create an Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook, and use Apache Spark ML to find duplicate records in the data. Evaluate and tune the model by evaluating performance and results of finding duplicates



Question # 9

A bank wants to migrate a Teradata data warehouse to the AWS Cloud The bank needs a solution for reading large amounts of data and requires the highest possible performance. The solution also must maintain the separation of storage and compute Which solution meets these requirements?

A. Use Amazon Athena to query the data in Amazon S3 
B. Use Amazon Redshift with dense compute nodes to query the data in Amazon Redshift managed storage 
C. Use Amazon Redshift with RA3 nodes to query the data in Amazon Redshift managed storage 
D. Use PrestoDB on Amazon EMR to query the data in Amazon S3 



Question # 10

A data analyst runs a large number of data manipulation language (DML) queries by using Amazon Athena with the JDBC driver. Recently, a query failed after It ran for 30 minutes.The query returned the following message Java.sql.SGLException: Query timeout The data analyst does not immediately need the query results However, the data analyst needs a long-term solution for this problem Which solution will meet these requirements?

A. Split the query into smaller queries to search smaller subsets of data. 
B. In the settings for Athena, adjust the DML query timeout limit 
C. In the Service Quotas console, request an increase for the DML query timeout 
D. Save the tables as compressed .csv files 



Question # 11

A bank is using Amazon Managed Streaming for Apache Kafka (Amazon MSK) to populate real-time data into a data lake The data lake is built on Amazon S3, and data must be accessible from the data lake within 24 hours Different microservices produce messages to different topics in the cluster The cluster is created with 8 TB of Amazon Elastic Block Store (Amazon EBS) storage and a retention period of 7 days The customer transaction volume has tripled recently and disk monitoring has provided an alert that the cluster is almost out of storage capacity What should a data analytics specialist do to prevent the cluster from running out of disk space1? 

A. Use the Amazon MSK console to triple the broker storage and restart the cluster 
B. Create an Amazon CloudWatch alarm that monitors the KafkaDataLogsDiskUsed metric Automatically flush the oldest messages when the value of this metric exceeds 85% 
C. Create a custom Amazon MSK configuration Set the log retention hours parameter to 48 Update the cluster with the new configuration file 
D. Triple the number of consumers to ensure that data is consumed as soon as it is added to a topic.



Question # 12

An online retail company uses Amazon Redshift to store historical sales transactions. The company is required to encrypt data at rest in the clusters to comply with the Payment Card Industry Data Security Standard (PCI DSS). A corporate governance policy mandates management of encryption keys using an on-premises hardware security module (HSM). Which solution meets these requirements? 

A. Create and manage encryption keys using AWS CloudHSM Classic. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management. 
B. Create a VPC and establish a VPN connection between the VPC and the on-premises network. Create an HSM connection and client certificate for the on-premises HSM. Launch a cluster in the VPC with the option to use the on-premises HSM to store keys. 
C. Create an HSM connection and client certificate for the on-premises HSM. Enable HSM encryption on the existing unencrypted cluster by modifying the cluster. Connect to the VPC where the Amazon Redshift cluster resides from the on-premises network using a VPN. 
D. Create a replica of the on-premises HSM in AWS CloudHSM. Launch a cluster in a VPC with the option to use CloudHSM to store keys.



Question # 13

A hospital uses an electronic health records (EHR) system to collect two types of data • Patient information, which includes a patient's name and address • Diagnostic tests conducted and the results of these tests Patient information is expected to change periodically Existing diagnostic test data never changes and only new records are added The hospital runs an Amazon Redshift cluster with four dc2.large nodes and wants to automate the ingestion of the patient information and diagnostic test data into respective Amazon Redshift tables for analysis The EHR system exports data as CSV files to an Amazon S3 bucket on a daily basis Two sets of CSV files are generated One set of files is for patient information with updates, deletes, and inserts The other set of files is for new diagnostic test data only What is the MOST cost-effective solution to meet these requirements? 

A. Use Amazon EMR with Apache Hudi. Run daily ETL jobs using Apache Spark and the Amazon Redshift JDBC driver 
B. Use an AWS Glue crawler to catalog the data in Amazon S3 Use Amazon Redshift Spectrum to perform scheduled queries of the data in Amazon S3 and ingest the data into the patient information table and the diagnostic tests table. 
C. Use an AWS Lambda function to run a COPY command that appends new diagnostic test data to the diagnostic tests table Run another COPY command to load the patient information data into the staging tables Use a stored procedure to handle create update, and delete operations for the patient information table 
D. Use AWS Database Migration Service (AWS DMS) to collect and process change data capture (CDC) records Use the COPY command to load patient information data into the staging tables. Use a stored procedure to handle create, update and delete operations for the patient information table



Amazon DAS-C01 Exam Reviews

    John JP         Feb 22, 2024

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