A company's marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
* The data size is approximately 32 TB uncompressed.
* There is a low volume of single-row inserts each day.
* There is a high volume of aggregation queries each day.
* Multiple complex joins are performed.
* The queries typically involve a small subset of the columns in a table.
Which storage service will provide the MOST performant solution?

• A.Amazon Neptune
• B.Amazon Elasticsearch
• C.Amazon Aurora MySQL
• D.Amazon Redshift

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A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time.
Which solution meets these requirements?

• A.Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.
• B.Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.
• C.Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.
• D.Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

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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 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.
• B.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.
• C.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.
• D.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.

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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.

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A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

• A.Convert the log files to Apace Avro format.
• B.Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
• C.Convert the log files to Apache Parquet format.
• D.Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
• E.Drop and recreate the table with the PARTITIONED BY clause. Run the ALTER TABLE ADD PARTITION statement.
• F.Drop and recreate the table with the PARTITIONED BY clause. Run the MSCK REPAIR TABLE statement.

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