A retail company uses Amazon Aurora MySQL for its operational data store and Amazon Redshift for its data warehouse. The MySQL database resides in a different VPC than the Redshift cluster. Data analysts need to query data in both MySQL and Amazon Redshift to provide business insights. The company wants the lowest network latency between the two VPCs.
Which combination of solutions meet these requirements? (Choose two.)
A. Set up VPC peering between the MySQL VPC and the Redshift VPC.An online gaming company wants to read customer data from Amazon Kinesis Data Streams and deliver the data to an Amazon S3 data lake for analytics. The data contains customer_id as one of the attributes. The data consumers need the data to be partitioned by customer_id in the S3 data lake.
Which solution will meet this requirement with the LEAST effort?
A. Create an Amazon Kinesis Data Firehose delivery stream. Use dynamic partitioning to partition the data by customer_id before delivering the data to Amazon S3.A mining company is using Amazon S3 as its data lake. The company wants to analyze the data collected by the sensors in its mines. A data pipeline is being built to capture data from the sensors, ingest the data into an S3 bucket, and convert the data to Apache Parquet format. The data pipeline must be processed in near-real time. The data will be used for on-demand queries with Amazon Athena.
Which solution will meet these requirements?
A. Use Amazon Kinesis Data Firehose to invoke an AWS Lambda function that converts the data to Parquet format and stores the data in Amazon S3.A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster.
A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the
structure of the JSON file. The company requires a solution with the least possible implementation effort.
Which combination of steps will create a solution that meets these requirements? (Choose three.)
A. Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.A company has an electronic healthcare system that contains data of patients. The data is consolidated from multiple systems and is stored in an Amazon S3 bucket in .csv format. The company has created an AWS Glue Data Catalog. The
dataset contains duplicate data, and no unique keys exist to identify a patient. Fields do not match exactly across the systems.
A data analytics specialist must design a solution to identify and remove duplicates. The solution must minimize the amount of human intervention and code that are required.
The data analytics specialist starts by using labeled data to teach the FindMatches machine learning (ML) transform.
What must the data analytics specialist do next to meet these requirements?
A. Identify matches in the dataset by using an AWS Glue ETL job with Spark distinct(). Review the output by using Amazon Redshift Spectrum.A company uses an Amazon QuickSight business intelligence (BI) dashboard to make decisions. A data warehouse that is hosted on an Amazon Redshift cluster is the primary data source for the dashboard.
The user experience has degraded because some BI queries are complex and include multiple table joins. A data analytics specialist needs to reduce query response times to improve the user experience and meet SLAs.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create automated materialized views. Turn on the query rewrite feature in Amazon Redshift.A financial services company hosts its data warehouse on a Hadoop cluster located in an on-premises data center. The data is 300 TB in size and grows by 1 TB each day. The data is generated in real time from the company's trading system. The raw data is transformed at the end of the trading day using a custom tool running on the Hadoop cluster.
The company is migrating its data warehouse to AWS using a managed data warehouse product provided by a third party that can ingest data from Amazon S3. The company has already established a 10 Gbps connection with an AWS Region using AWS Direct Connect. The company is required by its security and regulatory compliance policies to not transfer data over the public internet. The company wants to minimize changes to its custom tool for data transformation. The company also plans to eliminate the on-premises Hadoop cluster after the migration.
Which solution MOST cost-effectively meets these requirements?
A. Create a VPC endpoint for Amazon S3. Run a one-time copy job using the DistCp tool to copy existing files from Hadoop to a target S3 bucket over the VPC endpoint Schedule a nightly DistCp job on the Hadoop cluster to copy the incremental files produced by the custom tool to the target S3 bucketA company is attempting to use Amazon Athena to run analytics queries. The company is using the UNLOAD statement and has noticed that the result of the query is not appearing in the destination Amazon S3 bucket. The query previously ran successfully, and the company has not changed any configuration since then.
Which reason would prevent the query from running successfully?
A. The destination S3 bucket does not have the output folder.A company's system operators and security engineers need to analyze activities within specific date ranges of AWS CloudTrail logs. All log files are stored in an Amazon S3 bucket, and the size of the logs is more than 5 TB. The solution must be cost-effective and maximize query performance.
Which solution meets these requirements?
A. Copy the logs to a new S3 bucket with a prefix structure of . Use the date column as a partition key. Create a table on Amazon Athena based on the objects in the new bucket. Automatically add metadata partitions by using the MSCK REPAIR TABLE command in Athena. Use Athena to query the table and partitions.An IoT company is collecting data from multiple sensors and is streaming the data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type has its own topic, and each topic has the same number of partitions.
The company is planning to turn on more sensors. However, the company wants to evaluate which sensor types are producing the most data so that the company can scale accordingly. The company needs to know which sensor types have
the largest values for the following metrics: BytesInPerSec and MessagesInPerSec.
Which level of monitoring for Amazon MSK will meet these requirements?
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