| Exam Code | DAS-C01 |
| Exam Name | AWS Certified Data Analytics - Specialty |
| Questions | 157 |
| Update Date | September 22,2023 |
| Price |
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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
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.
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
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.
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.
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/".
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
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
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
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
