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A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.

Which solution will update the Redshift table without duplicates when jobs are rerun?

A. Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.

B. Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.

C. Use Apache Spark's DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.

D. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.

Which solution will improve the data loading performance?

A. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.

B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.

C. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

An online retail company with millions of users around the globe wants to improve its ecommerce analytics capabilities. Currently, clickstream data is uploaded directly to Amazon S3 as compressed files. Several times each day, an application running on Amazon EC2 processes the data and makes search options and reports available for visualization by editors and marketers. The company wants to make website clicks and aggregated data available to editors and marketers in minutes to enable them to connect with users more effectively.

Which options will help meet these requirements in the MOST efficient way? (Choose two.)

A. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

B. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon OpenSearch Service (Amazon Elasticsearch Service) from Amazon S3.

C. Use Amazon OpenSearch Service (Amazon Elasticsearch Service) deployed on Amazon EC2 to aggregate, filter, and process the data. Refresh content performance dashboards in near-real time.

D. Use OpenSearch Dashboards (Kibana) to aggregate, filter, and visualize the data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service). Refresh content performance dashboards in near-real time.

E. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon OpenSearch Service (Amazon Elasticsearch Service).

A data analytics specialist is maintaining a company's on-premises Apache Hadoop environment. In this environment, the company uses Apache Spark jobs for data transformation and uses Apache Presto for on-demand queries. The Spark

jobs consist of many intermediate steps that require high-speed random I/O during processing. Some jobs can be restarted without losing the original data.

The data analytics specialist decides to migrate the workload to an Amazon EMR cluster. The data analytics specialist must implement a solution that will scale the cluster automatically.

Which solution will meet these requirements with the FASTEST I/O?

A. Use Hadoop Distributed File System (HDFS). Configure the EMR cluster as an instance fleet with custom automatic scaling.

B. Use EMR File System (EMRFS). Configure the EMR cluster as a uniform instance group with EMR managed scaling.

C. Use Hadoop Distributed File System (HDFS). Configure the EMR cluster as an instance group with custom automatic scaling.

D. Use EMR File System (EMRFS). Configure the EMR cluster as an instance fleet with custom automatic scaling.

A company has a fitness tracker application that generates data from subscribers. The company needs real-time reporting on this data. The data is sent immediately, and the processing latency must be less than 1 second. The company wants to perform anomaly detection on the data as the data is collected. The company also requires a solution that minimizes operational overhead.

Which solution meets these requirements?

A. Amazon EMR cluster with Apache Spark streaming, Spark SQL, and Spark's machine learning library (MLlib)

B. Amazon Kinesis Data Firehose with Amazon S3 and Amazon Athena

C. Amazon Kinesis Data Firehose with Amazon QuickSight

D. Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics