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A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals. The company wants to run an additional analysis on a year’s worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week. What is the MOST cost-effective solution?
A bank is building an Amazon S3 data lake. The bank wants a single data repository for customer data needs, such as personalized recommendations. The bank needs to use Amazon Kinesis Data Firehose to ingest customers' personal information, bank accounts, and transactions in near real time from a transactional relational database. All personally identifiable information (Pll) that is stored in the S3 bucket must be masked. The bank has enabled versioning for the S3 bucket. Which solution will meet these requirements?
A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day. A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data. How can the data analyst meet these requirements with the lowest maintenance overhead?
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?
A data engineer is using AWS Glue ETL jobs to process data at frequent intervals. The processed data is then copied into Amazon S3. The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job. Which solution will meet these requirements MOST cost-effectively?
A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements. Which solution meets the company’s requirements?
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A company uses Amazon Redshift for its data warehouse. The company is running an ET L process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job. The company receives the data parts at various times throughout each day. A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts. Which solution will meet these requirements?
A company needs to implement a near-real-time messaging system for hotel inventory. The messages are collected from 1,000 data sources and contain hotel inventory data. The data is then processed and distributed to 20 HTTP endpoint destinations. The range of data size for messages is 2-500 KB. The messages must be delivered to each destination in order. The performance of a single destination HTTP endpointshould not impact the performance of the delivery for other destinations. Which solution meets these requirements with the LOWEST latency from message ingestion to delivery?

Question 165 - DAS-C01 discussion

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A social media company is using business intelligence tools to analyze data for forecasting. The company is using Apache Kafka to ingest data. The company wants to build dynamic dashboards that include machine learning (ML) insights to forecast key business trends.

The dashboards must show recent batched data that is not more than 75 minutes old. Various teams at the company want to view the dashboards by using Amazon QuickSight with ML insights.

Which solution will meet these requirements?

A.
Replace Kafka with Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.
Answers
A.
Replace Kafka with Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.
B.
Replace Kafka with an Amazon Kinesis data stream. Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Standard edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.
Answers
B.
Replace Kafka with an Amazon Kinesis data stream. Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Standard edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.
C.
Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.
Answers
C.
Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.
D.
Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Refresh the data in QuickSight Standard edition SPICE from Amazon S3 by using a scheduled AWS Lambda function. Configure the Lambda function to run every 75 minutes and to invoke the QuickSight API to create a dynamic dashboard that includes forecasting and ML insights.
Answers
D.
Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Refresh the data in QuickSight Standard edition SPICE from Amazon S3 by using a scheduled AWS Lambda function. Configure the Lambda function to run every 75 minutes and to invoke the QuickSight API to create a dynamic dashboard that includes forecasting and ML insights.
Suggested answer: C
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asked 16/09/2024
Reselan Govender
33 questions
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