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The AWS Certified Data Analytics - Specialty (DAS-C01) certification exam is designed to test the skills and knowledge of individuals who work with data analytics on the Amazon Web Services (AWS) platform. DAS-C01 exam is intended for professionals who have experience with AWS services related to data analytics, such as Amazon Redshift, Amazon Kinesis, and Amazon EMR. The DAS-C01 certification exam is a great way to demonstrate your expertise in this field and showcase your ability to work with data in AWS.

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Amazon AWS-Certified-Data-Analytics-Specialty (AWS Certified Data Analytics - Specialty (DAS-C01)) Certification Exam is designed for individuals who want to showcase their skills and knowledge in the field of data analytics. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification exam is for those who have a deep understanding of data analysis, data visualization, and data management using AWS services. AWS Certified Data Analytics - Specialty (DAS-C01) Exam certification is created for professionals who want to validate their expertise in designing, building, securing, and maintaining analytics solutions on AWS.

Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
A transport company wants to track vehicular movements by capturing geolocation records. The records are
10 B in size and up to 10,000 records are captured each second. Data transmission delays of a few minutes are acceptable, considering unreliable network conditions. The transport company decided to use Amazon Kinesis Data Streams to ingest the data. The company is looking for a reliable mechanism to send data to Kinesis Data Streams while maximizing the throughput efficiency of the Kinesis shards.
Which solution will meet the company's requirements?

A. Kinesis Agent
B. Kinesis Producer Library (KPL)
C. Kinesis Data Firehose
D. Kinesis SDK

Answer: B

NEW QUESTION # 34
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 year, month, and day.
B. Store the data in Apache Avro format using Snappy compression.
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.

Answer: C,D

NEW QUESTION # 35
A marketing company collects clickstream data The company sends the data to Amazon Kinesis Data Firehose and stores the data in Amazon S3 The company wants to build a series of dashboards that will be used by hundreds of users across different departments The company will use Amazon QuickSight to develop these dashboards The company has limited resources and wants a solution that could scale and provide daily updates about clickstream activity Which combination of options will provide the MOST cost-effective solution? (Select TWO )

A. Use S3 analytics to query the clickstream data
B. Use QuickSight with a direct SQL query
C. Use the QuickSight SPICE engine with a daily refresh
D. Use Amazon Athena to query the clickstream data in Amazon S3
E. Use Amazon Redshift to store and query the clickstream data

Answer: A,B

NEW QUESTION # 36
A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?

A. There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
B. The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
C. The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.
D. The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.

Answer: C

Explanation:
Explanation
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-after-resharding.html the parent shards that remain after the reshard could still contain data that you haven't read yet that was added to the stream before the reshard. If you read data from the child shards before having read all data from the parent shards, you could read data for a particular hash key out of the order given by the data records' sequence numbers.
Therefore, assuming that the order of the data is important, you should, after a reshard, always continue to read data from the parent shards until it is exhausted. Only then should you begin reading data from the child shards.

NEW QUESTION # 37
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 Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluster. Visualize the data by using a Kibana dashboard.
B. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
C. 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.
D. Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Answer: C

NEW QUESTION # 38
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