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NEW QUESTION 46
A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.
Which solution meets these requirements?

• A. Use Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a custom application for the dashboard by using D3.js.
• B. Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
• C. Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluster. Visualize the data by using a Kibana dashboard.
• D. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Answer: A

NEW QUESTION 47
A media content company has a streaming playback application. The company wants to collect and analyze the data to provide near-real-time feedback on playback issues. The company needs to consume this data and return results within 30 seconds according to the service-level agreement (SLA). The company needs the consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as JSON and may change schemas over time.
Which solution will allow the company to collect data for processing while meeting these requirements?

• A. Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
• B. Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.
• C. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to trigger an event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.
• D. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event trigger an AWS Lambda function to process the data. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon S3.

Answer: B

Explanation:
https://aws.amazon.com/blogs/aws/new-amazon-kinesis-data-analytics-for-java/

NEW QUESTION 48
A company wants to run analytics on its Elastic Load Balancing logs stored in Amazon S3. A data analyst needs to be able to query all data from a desired year, month, or day. The data analyst should also be able to query a subset of the columns. The company requires minimal operational overhead and the most cost-effective solution.
Which approach meets these requirements for optimizing and querying the log data?

• A. Use an AWS Glue job nightly to transform new log files into Apache Parquet format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
• B. Launch a long-running Amazon EMR cluster that continuously transforms new log files from Amazon S3 into its Hadoop Distributed File System (HDFS) storage and partitions by year, month, and day. Use Apache Presto to query the optimized format.
• C. Use an AWS Glue job nightly to transform new log files into .csv format and partition by year, month, and day. Use AWS Glue crawlers to detect new partitions. Use Amazon Athena to query data.
• D. Launch a transient Amazon EMR cluster nightly to transform new log files into Apache ORC format and partition by year, month, and day. Use Amazon Redshift Spectrum to query the data.

Answer: D

NEW QUESTION 49
A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL.
Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams.
Which solution meets these requirements?

• A. Increase the query priority to HIGHEST for the data analysis queue.
• B. Configure the data analysis queue to enable concurrency scaling.
• C. Create a query monitoring rule to add more cluster capacity for the data analysis queue when queries are waiting for resources.
• D. Use workload management query queue hopping to route the query to the next matching queue.

Answer: D

NEW QUESTION 50
A gaming company is collecting cllckstream data into multiple Amazon Kinesis data streams. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3 Data scientists use Amazon Athena to query the most recent data and derive business insights. The company wants to reduce its Athena costs without having to recreate the data pipeline. The company prefers a solution that will require less management effort Which set of actions can the data scientists take immediately to reduce costs?

• A. Create an Apache Spark Job that combines and converts JSON files to Apache Parquet files Launch an Amazon EMR ephemeral cluster daily to run the Spark job to create new Parquet files in a different S3 location Use ALTER TABLE SET LOCATION to reflect the new S3 location on the existing Athena table.
• B. Create a Kinesis data stream as a delivery target for Kinesis Data Firehose Run Apache Flink on Amazon Kinesis Data Analytics on the stream to read the streaming data, aggregate ikand save it to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table
• C. Integrate an AWS Lambda function with Kinesis Data Firehose to convert source records to Apache Parquet and write them to Amazon S3 In parallel, run an AWS Glue ETL job to combine and convert existing JSON files to large Parquet files Create a custom S3 object YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.
• D. Change the Kinesis Data Firehose output format to Apache Parquet Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size For the existing data, run an AWS Glue ETL job to combine and convert small JSON files to large Parquet files and add the YYYYMMDD prefix Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

Answer: C

NEW QUESTION 51
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