Exam 4: AWS Certified Data Analytics - Specialty (DAS-C01)

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 retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users. The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB. Which configuration will provide the MOST cost-effective solution that meets these requirements?

A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records. Which solution will accomplish this task?

A company is building a data lake and needs to ingest data from a relational database that has time-series data. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3. What is the MOST cost-effective approach to meet these requirements?

An ecommerce company is migrating its business intelligence environment from on premises to the AWS Cloud. The company will use Amazon Redshift in a public subnet and Amazon QuickSight. The tables already are loaded into Amazon Redshift and can be accessed by a SQL tool. The company starts QuickSight for the first time. During the creation of the data source, a data analytics specialist enters all the information and tries to validate the connection. An error with the following message occurs: "Creating a connection to your data source timed out." How should the data analytics specialist resolve this error?

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 mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units. Which steps should a data analyst take to accomplish this task efficiently and securely?

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling. More testing facilities were recently added, and the time to process files is increasing. What will MOST efficiently decrease the data processing time?

A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service. The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture. Which actions should the data analyst take to resolve this issue? (Choose two.)

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?

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 company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner. Which solution meets these requirements?

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 company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run. Which approach would allow the developers to solve the issue with minimal coding effort?

A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day. Which solution will improve the data loading performance?