| Exam Code | DAS-C01 |
| Exam Name | AWS Certified Data Analytics - Specialty |
| Questions | 157 |
| Update Date | March 20,2023 |
| Price |
Was : |
Our most skilled and experienced professionals are providing updated and accurate study material in PDF form to our customers. So that they can get more than 80% marks in the Amazon DAS-C01 exam. Our professional keeps updated to our customers if there is change in the DAS-C01 dumps PDF file. You and your money are very valuable and there is not a 1% chance to ruin it.
You can get an agent for your guidance 24/7. Our agent will provide you information you need for your satisfaction. We are here to provide you with all the study material you need to pass your DAS-C01 exam with remarkable marks.
Pass4surexams is the best source of exam preparation materials that can help you pass the DAS-C01 Exam. With our comprehensive DAS-C01 Dumps PDF, you can be sure that you will pass your exam with high marks. Our experts have created these materials to ensure that all candidates have the right knowledge and skills needed to ace their exams. We guarantee that when you use our resources, you'll be one step closer to success in your Amazon certification journey.
DAS-C01 is a certification exam offered by Amazon Web Services (AWS) for the AWS Certified Data Analytics - Specialty certification. This exam measures the candidate's knowledge and skills in designing and implementing AWS services to derive value from data. Here are some of the topics that you will learn when preparing for the DAS-C01 exam:
In summary, DAS-C01 covers a broad range of topics related to AWS data analytics services and their application to real-world scenarios. By passing this exam, you will demonstrate your expertise in designing and implementing data analytics solutions on AWS.
Passing the DAS-C01 exam is not an easy task. It requires a lot of hard work and dedication to understand the concepts and gain the necessary skills. Pass4surexams provides you with all the necessary resources to help you prepare for your exam in an effective and efficient manner. Our DAS-C01 study materil provide you with DAS-C01 real exam questions and answers that are designed to help you to pass your exam in first attempt. With our DAS-C01 PDF Questions, you can easily study for your exam anytime, anywhere, as they are available in PDF format. Furthermore, our DAS-C01 Dumps PDF also contains explanations and answers that will help you better understand the concepts covered in the exam. With Pass4surexams, passing your DAS-C01 Exam will be a breeze.
Pass4surexams have just the right thing for you - our 90 Days Updated DAS-C01 Free Exam Questions. This collection of exam dumps is designed to help you pass your exam with ease and accuracy. Our team of experts has compiled these questions from real exams and verified them to ensure that they are up-to-date and accurate. With our DAS-C01 PDF Questions, you can be sure that you will get the most out of your studying time and be able to pass your exam with flying colors.
Refund policy is an important part of any purchase. It helps customers to get back their money in case of failure. When it comes to DAS-C01 Exam Dumps, we understand that customers may not be able to pass the exam after purchasing our product. That’s why we have a refund policy in place for our customers who fail the exam after using our product.
If you fail the DAS-C01 Exam after using our DAS-C01 PDF Questions, then you can claim a full refund within 15 days of purchase. All you need to do is submit proof of your result and provide us with your order details. We will process your request and issue a full refund as soon as possible.
A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.
After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically.
What should the data analyst do to resolve this?
A. Increase the number of threads that process the stream records.
B. Increase the provisioned read capacity units assigned to the stream’s Amazon DynamoDB table.
C. Increase the provisioned write capacity units assigned to the stream’s Amazon DynamoDB table.
D. Decrease the provisioned write capacity units assigned to the stream’s Amazon DynamoDB table.
Answer: C
A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.
Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?
A. Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.
B. Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.
C. Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.
D. Use AWS WAF to block public internet access to the EMR clusters across the board.
Answer: C
Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-block-public-access.html
A company wants to collect and process events data from different departments in nearreal time. Before storing the data in Amazon S3, the company needs to clean the data by standardizing the format of the address and timestamp columns. The data varies in size based on the overall load at each particular point in time. A single data record can be 100 KB-10 MB.
How should a data analytics specialist design the solution for data ingestion?
A. Use Amazon Kinesis Data Streams. Configure a stream for the raw data. Use a Kinesis Agent to write data to the stream. Create an Amazon Kinesis Data Analytics application that reads data from the raw stream, cleanses it, and stores the output to Amazon S3.
B. Use Amazon Kinesis Data Firehose. Configure a Firehose delivery stream with a preprocessing AWS Lambda function for data cleansing. Use a Kinesis Agent to write data to the delivery stream. Configure Kinesis Data Firehose to deliver the data to Amazon S3.
C. Use Amazon Managed Streaming for Apache Kafka. Configure a topic for the raw data. Use a Kafka producer to write data to the topic. Create an application on Amazon EC2 that reads data from the topic by using the Apache Kafka consumer API, cleanses the data, and writes to Amazon S3.
D. Use Amazon Simple Queue Service (Amazon SQS). Configure an AWS Lambda function to read events from the SQS queue and upload the events to Amazon S3.
Answer: B
A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company’s analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)
A. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.
B. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.
C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.
D. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.
E. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.
Answer: B,C
Reference:
https://www.upsolver.com/blog/apache-parquet-why-use
https://aws.amazon.com/blogs/big-data/work-with-partitioned-data-in-aws-glue/
A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog.
Which solution meets these requirements?
A. Create IAM groups and resource tags for each team within the company. Set up IAM policies that control user access and actions on the Data Catalog resources.
B. Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.
C. Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.
D. Create Athena query groups for each team within the company and assign users to the groups.
Answer: C
Explanation:
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes. The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?
A. Concurrency scaling
B. Short query acceleration (SQA)
C. Workload management (WLM)
D. Materialized views
Answer: D
Explanation:
Materialized views:
Reference:
https://aws.amazon.com/redshift/faqs/
A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly Once a new month begins, no new writes are made to any of the indices from the previous months The company has been expanding the storage on the Amazon OpenSearch Service {Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs Most searches on the cluster are on the most recent 3 months of data while the audit team requires infrequent access to older data to generate periodic reports The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs.
Which of the following is the MOST operationally efficient solution to meet these requirements?
A. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier When the audit team requires the archived data restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster
B. Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3 When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster
C. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage
D. Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage When the audit team requires the older data: migrate the indices in UltraWarm storage back to hot storage
Answer: D
A team of data scientists plans to analyze market trend data for their company’s new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?
A. Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
B. Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
C. Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
D. Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
Answer: B
An education provider’s learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider’s LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance. System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?
A. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
B. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.
C. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query data in the data lake. Ensure the S3 Standard storage class is in use with objects in the data lake.
D. Store the most recent 4 months of data in the Amazon Redshift cluster. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce costs. Ensure the S3 Standard storage class is in use with objects in the data lake.
Answer: C
Reference:
https://aws.amazon.com/redshift/pricing/
A media analytics company consumes a stream of social media posts. The posts are sent to an Amazon Kinesis data stream partitioned on user_id. An AWS Lambda function retrieves the records and validates the content before loading the posts into an Amazon Elasticsearch cluster. The validation process needs to receive the posts for a given user in the order they were received. A data analyst has noticed that, during peak hours, the social media platform posts take more than an hour to appear in the Elasticsearch cluster.
What should the data analyst do reduce this latency?
A. Migrate the validation process to Amazon Kinesis Data Firehose.
B. Migrate the Lambda consumers from standard data stream iterators to an HTTP/2 stream consumer.
C. Increase the number of shards in the stream.
D. Configure multiple Lambda functions to process the stream.
Answer: D
