AWS-Certified-Data-Analytics-Specialty Free Practice Test

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis. The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

1. A. Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehos
2. B. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data inthe DynamoDB tabl
3. C. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
4. D. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
5. E. Use AWS Glue crawlers to catalog the log
6. F. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the dat
7. G. Store the enriched data in Amazon S3.
8. H. Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Stream
9. I. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the sourc
10. J. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
11. K. Export the raw logs to Amazon S3 on an hourly basis using the AWS CL
12. L. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoD
13. M. Store the enriched data in Amazon S3.

Correct Answer: A
https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#FirehoseExample

A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

1. A. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enable
2. B. Use Amazon EMR running PySpark to process the data in Amazon S3.
3. C. Set up an AWS Lambda function with a Python runtime environmen
4. D. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
5. E. Launch an Amazon Redshift cluste
6. F. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
7. G. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet forma
8. H. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.

Correct Answer: D
https://aws.amazon.com/it/about-aws/whats-new/2020/04/aws-glue-now-supports-serverless-streaming-etl/

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.)

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

Correct Answer: CD

A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.
Which approach is the MOST efficient way to meet these requirements?

1. A. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneousl
2. B. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold valu
3. C. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
4. D. Use Amazon Kinesis Data Streams to collect all the data from toll station
5. E. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold valu
6. F. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not me
7. G. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.
8. H. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshif
9. I. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
10. J. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneousl
11. K. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.

Correct Answer: D

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?

1. A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
2. B. Enable job bookmarks on the AWS Glue jobs.
3. C. Create custom logic on the ETL jobs to track the processed S3 objects.
4. D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Correct Answer: B