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NEW QUESTION 28
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 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.
B. 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.
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. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
D. Leverage DS2 nodes for the Amazon Redshift cluster. Migrate all data from Amazon S3 to Amazon Redshift. Decommission the data lake.

Answer: B

NEW QUESTION 29
A financial services company needs to aggregate daily stock trade data from the exchanges into a data store.
The company requires that data be streamed directly into the data store, but also occasionally allows data to be modified using SQL. The solution should integrate complex, analytic queries running with minimal latency.
The solution must provide a business intelligence dashboard that enables viewing of the top contributors to anomalies in stock prices.
Which solution meets the company's requirements?

A. Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
B. Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.
C. Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.
D. Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.

Answer: C

NEW QUESTION 30
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?

A. Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3 Standard- Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed.
Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival
7 days after the last date the object was accessed.
B. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.
C. Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation.
Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival
7 days after object creation.
D. Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Configure a lifecycle policy to move the data into the Amazon S3 Standard- Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Configure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.

Answer: C

NEW QUESTION 31
A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

A. Set up an AWS Lambda function with a Python runtime environment. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
B. Launch an Amazon Redshift cluster. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
C. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet format. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.
D. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enabled. Use Amazon EMR running PySpark to process the data in Amazon S3.

Answer: C

Explanation:
Explanation
https://aws.amazon.com/it/about-aws/whats-new/2020/04/aws-glue-now-supports-serverless-streaming-etl/

NEW QUESTION 32
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?