100% PASS QUIZ 2025 AWS-CERTIFIED-DATA-ANALYTICS-SPECIALTY: AWS CERTIFIED DATA ANALYTICS - SPECIALTY (DAS-C01) EXAM–EFFICIENT PRACTICE TEST

100% Pass Quiz 2025 AWS-Certified-Data-Analytics-Specialty: AWS Certified Data Analytics - Specialty (DAS-C01) Exam–Efficient Practice Test

100% Pass Quiz 2025 AWS-Certified-Data-Analytics-Specialty: AWS Certified Data Analytics - Specialty (DAS-C01) Exam–Efficient Practice Test

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Amazon AWS Certified Data Analytics - Specialty (DAS-C01) Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.
Which solution will accomplish this task?

  • A. Amazon SageMaker Ground Truth
  • B. An AWS Glue ETL job with the ResolveChoice transform
  • C. Amazon Kendra
  • D. An AWS Glue ETL job with the FindMatches transform

Answer: D

Explanation:
Explanation
Matching Records with AWS Lake Formation FindMatches


NEW QUESTION # 131
A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.
Which solution meets these requirements?

  • A. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.
  • B. Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.
  • C. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.
    Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.
  • D. Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function.
    Save the output to DynamoDB by using the default output from Kinesis Data Firehose.

Answer: D


NEW QUESTION # 132
A medical company has a system with sensor devices that read metrics and send them in real time to an Amazon Kinesis data stream. The Kinesis data stream has multiple shards. The company needs to calculate the average value of a numeric metric every second and set an alarm for whenever the value is above one threshold or below another threshold. The alarm must be sent to Amazon Simple Notification Service (Amazon SNS) in less than 30 seconds.
Which architecture meets these requirements?

  • A. Use an Amazon Kinesis Data Firehose delivery stream to read the data from the Kinesis data stream with an AWS Lambda transformation function that calculates the average per second and sends the alarm to Amazon SNS.
  • B. Use an Amazon Kinesis Data Analytics application to read from the Kinesis data stream and calculate the average per second. Send the results to an AWS Lambda function that sends the alarm to Amazon SNS.
  • C. Use an Amazon Kinesis Data Firehose deliver stream to read the data from the Kinesis data stream and store it on Amazon S3. Have Amazon S3 trigger an AWS Lambda function that calculates the average per second and sends the alarm to Amazon SNS.
  • D. Use an AWS Lambda function to read from the Kinesis data stream to calculate the average per second and sent the alarm to Amazon SNS.

Answer: B


NEW QUESTION # 133
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. Upload clickstream records from Amazon S3 to Amazon Kinesis Data Streams and use a Kinesis Data Streams consumer to send records to Amazon Elasticsearch Service.
  • B. Use Kibana to aggregate, filter, and visualize the data stored in Amazon Elasticsearch Service. Refresh content performance dashboards in near-real time.
  • C. Use Amazon Elasticsearch Service deployed on Amazon EC2 to aggregate, filter, and process the data.
    Refresh content performance dashboards in near-real time.
  • D. Upload clickstream records to Amazon S3 as compressed files. Then use AWS Lambda to send data to Amazon Elasticsearch Service from Amazon S3.
  • E. Use Amazon Kinesis Data Firehose to upload compressed and batched clickstream records to Amazon Elasticsearch Service.

Answer: B,E


NEW QUESTION # 134
A company's system operators and security engineers need to analyze activities within specific date ranges of AWS CloudTrail logs. All log files are stored in an Amazon S3 bucket, and the size of the logs is more than 5 T B. The solution must be cost-effective and maximize query performance.
Which solution meets these requirements?

  • A. Create an AWS Glue job to copy the logs from the S3 source bucket to a new S3 bucket and create a table using Apache Parquet file format, Snappy as compression codec, and partition by date. Use Amazon Athena to query the table and partitions.
  • B. Launch an Amazon EMR cluster and use Amazon S3 as a data store for Apache HBase. Load the logs from the S3 bucket to an HBase table on Amazon EMR. Use Amazon Athena to query the table and partitions.
  • C. Create a table on Amazon Athena. Manually add metadata partitions by using the ALTER TABLE ADD PARTITION statement, and use multiple columns for the partition key. Use Athena to query the table and partitions.
  • D. Copy the logs to a new S3 bucket with a prefix structure of <PARTITION COLUMN_NAME>. Use the date column as a partition key. Create a table on Amazon Athena based on the objects in the new bucket. Automatically add metadata partitions by using the MSCK REPAIR TABLE command in Athena. Use Athena to query the table and partitions.

Answer: A

Explanation:
This solution meets the requirements because:
AWS Glue is a fully managed extract, transform, and load (ETL) service that can be used to prepare and load data for analytics1. You can use AWS Glue to create a job that copies the CloudTrail logs from the source S3 bucket to a new S3 bucket, and converts them to Apache Parquet format2. Parquet is a columnar storage format that is optimized for analytics and supports compression3. Snappy is a compression codec that provides a good balance between compression ratio and speed4.
AWS Glue can also create a table based on the Parquet files in the new S3 bucket, and partition the table by date2. Partitioning is a technique that divides a large dataset into smaller subsets based on a partition key, such as date5. Partitioning can improve query performance by reducing the amount of data scanned and filtering out irrelevant data5.
Amazon Athena is an interactive query service that allows you to analyze data in S3 using standard SQL6. You can use Athena to query the table created by AWS Glue, and specify the partitions you want to query based on the date range. Athena can leverage the benefits of Parquet format and partitioning to run queries faster and more cost-effectively.


NEW QUESTION # 135
......

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