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NEW QUESTION 40 A company has several Amazon EC2 instances sitting behind an Application Load Balancer (ALB) The company wants its IT Infrastructure team to analyze the IP addresses coming into the company's ALB The ALB is configured to store access logs in Amazon S3 The access logs create about 1 TB of data each day, and access to the data will be infrequent The company needs a solution that is scalable, cost-effective and has minimal maintenance requirements Which solution meets these requirements?

• A. Copy the data into Amazon Redshift and query the data
• B. Use Amazon EMR and Apache Hive to query the S3 data
• C. Use Amazon Redshift Spectrum to query the S3 data
• D. Use Amazon Athena to query the S3 data

Answer: C   NEW QUESTION 41 A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud. The company needs a solution that offers near-real-time analytics on the data from the most updated sensors. Which solution enables the company to meet these requirements?

• A. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon Elasticsearch Service cluster.
• B. Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.
• C. Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.
• D. Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side. Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten the JSON file before sending it to an Amazon S3 bucket. Load the S3 data into an Amazon Redshift cluster.

Answer: B   NEW QUESTION 42 A university intends to use Amazon Kinesis Data Firehose to collect JSON-formatted batches of water quality readings in Amazon S3. The readings are from 50 sensors scattered across a local lake. Students will query the stored data using Amazon Athena to observe changes in a captured metric over time, such as water temperature or acidity. Interest has grown in the study, prompting the university to reconsider how data will be stored. Which data format and partitioning choices will MOST significantly reduce costs? (Choose two.)

• A. Partition the data by sensor, year, month, and day.
• B. Store the data in Apache Avro format using Snappy compression.
• C. Store the data in Apache Parquet format using Snappy compression.
• D. Store the data in Apache ORC format using no compression.
• E. Partition the data by year, month, and day.

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