1. A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.Which combination of steps would meet these requirements? (Choose two.)(Select 2answers)

A) Use the COPY command with the manifest file to load data into Amazon Redshift.
B) Use S3DistCp to load files into Amazon Redshift.
C) Use temporary staging tables during the loading process.
D) Use the UNLOAD command to upload data into Amazon Redshift.
E) Use Amazon Redshift Spectrum to query files from Amazon S3.


2. 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.)(Select 2answers)

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


3. A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.When defining tables in the Data Catalog, the company has the following requirements:' Choose the catalog table name and do not rely on the catalog table naming algorithm.' Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.Which solution meets these requirements with minimal effort?

A) Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
B) Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.
C) Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
D) Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.



4. An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used AmazonAthena for querying the data. As the amount of data increases, the data analyst wants to optimize the storage solution to improve query performance.Which options should the data analyst use to improve performance as the data lake grows? (Choose three.)(Select 3answers)

A) Add a randomized string to the beginning of the keys in S3 to get more throughput across partitions.
B) Use an S3 bucket in the same account as Athena.
C) Compress the objects to reduce the data transfer I/O.
D) Use an S3 bucket in the same Region as Athena.
E) Preprocess the .csv data to JSON to reduce I/O by fetching only the document keys needed by the query.
F) Preprocess the .csv data to Apache Parquet to reduce I/O by fetching only the data blocks needed for predicates.

5. A company uses the Amazon Kinesis SDK to write data to Kinesis Data Streams. Compliance requirements state that the data must be encrypted at rest using a key that can be rotated. The company wants to meet this encryption requirement with minimal coding effort.How can these requirements be met?

A) Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Use the AWS Encryption SDK, providing it with the key alias to encrypt and decrypt the data.
B) Create a customer master key (CMK) in AWS KMS. Assign the CMK an alias. Enable server-side encryption on the Kinesis data stream using the CMK alias as the KMS master key.
C) Create a customer master key (CMK) in AWS KMS. Create an AWS Lambda function to encrypt and decrypt the data. Set the KMS key ID in the function's environment variables.
D) Enable server-side encryption on the Kinesis data stream using the default KMS key for Kinesis Data Streams.