[2024] DAS-C01 by AWS Certified Data Analytics Actual Free Exam Practice Test [Q22-Q44]

[2024]  DAS-C01 by AWS Certified Data Analytics Actual Free Exam Practice Test

Free AWS Certified Data Analytics DAS-C01 Exam Question

To prepare for the AWS Certified Data Analytics - Specialty certification exam, candidates should have experience with AWS services and data analytics. Candidates should also have a strong understanding of data analysis tools and techniques, such as data modeling, data visualization, and statistical analysis. Candidates can prepare for the exam by taking online courses, attending training sessions, and studying AWS documentation.

The AWS Certified Data Analytics - Specialty exam consists of 65 multiple-choice and multiple-response questions and is conducted in a proctored environment. DAS-C01 exam covers a wide range of topics, including data collection, processing, storage, and analysis using AWS services such as Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis. Candidates are also tested on their ability to design and implement secure and scalable data analytics solutions using AWS services.

 

NEW QUESTION # 22
An online retailer needs to deploy a product sales reporting solution. The source data is exported from an external online transaction processing (OLTP) system for reporting. Roll-up data is calculated each day for the previous day's activities. The reporting system has the following requirements:
Have the daily roll-up data readily available for 1 year.
After 1 year, archive the daily roll-up data for occasional but immediate access.
The source data exports stored in the reporting system must be retained for 5 years. Query access will be needed only for re-evaluation, which may occur within the first 90 days.
Which combination of actions will meet these requirements while keeping storage costs to a minimum?
(Choose two.)

• A. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 1 year after data creation.
• B. Store the source data initially in the Amazon S3 Glacier storage class. Apply a lifecycle configuration that changes the storage class from Amazon S3 Glacier to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
• C. Store the source data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier Deep Archive 90 days after creation, and then deletes the data 5 years after creation.
• D. Store the daily roll-up data initially in the Amazon S3 Standard storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Standard-Infrequent Access (S3 Standard-IA)
  1 year after
  data creation.
• E. Store the daily roll-up data initially in the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Apply a lifecycle configuration that changes the storage class to Amazon S3 Glacier 1 year after data creation.

Answer: C,D

NEW QUESTION # 23
A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?

• A. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.
• B. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
  Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.
• C. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.
• D. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.

Answer: A

NEW QUESTION # 24
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 AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.
• B. 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.
• 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 Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Answer: C

NEW QUESTION # 25
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. Split large .csv files, then use a COPY command to load data into Amazon Redshift.
• B. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.
• C. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.
• D. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/redshift/latest/dg/c_loading-data-best-practices.html

NEW QUESTION # 26
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. 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.
• 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. 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.

Answer: C

Explanation:
Updating Manually Created Data Catalog Tables Using Crawlers: To do this, when you define a crawler, instead of specifying one or more data stores as the source of a crawl, you specify one or more existing Data Catalog tables. The crawler then crawls the data stores specified by the catalog tables. In this case, no new tables are created; instead, your manually created tables are updated.

NEW QUESTION # 27
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
* Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
* One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data?
(Choose three.)

• A. For archived data, use Amazon EMR to perform data transformations.
• B. For daily incoming data, use Amazon Athena to scan and identify the schema.
• C. For daily incoming data, use Amazon Redshift to perform transformations.
• D. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
• E. For archived data, use Amazon SageMaker to perform data transformations.
• F. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.

Answer: A,D,F

NEW QUESTION # 28
A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data? (Choose three.)

• A. For archived data, use Amazon EMR to perform data transformations.
• B. For daily incoming data, use Amazon Athena to scan and identify the schema.
• C. For daily incoming data, use Amazon Redshift to perform transformations.
• D. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
• E. For archived data, use Amazon SageMaker to perform data transformations.
• F. For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.

Answer: A,D,F

NEW QUESTION # 29
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.
Which solution meets these requirements?

• A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the data catalog in Aurora. Schedule the Lambda functions periodically.
• B. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalog. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.
• C. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata changes. Schedule the crawlers periodically to update the metadata catalog.
• D. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalog. Schedule the Lambda functions periodically.

Answer: B

NEW QUESTION # 30
An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical dat a. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?

• A. Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster. Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.
• B. Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS. Run historical queries using Amazon Athena.
• C. Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.
• D. Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.

Answer: D

Explanation:
Section: (none)
Explanation

NEW QUESTION # 31
A company has an application that ingests streaming data. The company needs to analyze this stream over a
5-minute timeframe to evaluate the stream for anomalies with Random Cut Forest (RCF) and summarize the current count of status codes. The source and summarized data should be persisted for future use.
Which approach would enable the desired outcome while keeping data persistence costs low?

• A. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 1 minute or 1 MB in Amazon S3. Ensure Amazon S3 triggers an event to invoke an AWS Lambda consumer that evaluates the batch data, collects the number status codes, and evaluates the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.
• B. Ingest the data stream with Amazon Kinesis Data Streams. Have a Kinesis Data Analytics application evaluate the stream over a 5-minute window using the RCF function and summarize the count of status codes. Persist the source and results to Amazon S3 through output delivery to Kinesis Data Firehouse.
• C. Ingest the data stream with Amazon Kinesis Data Streams. Have an AWS Lambda consumer evaluate the stream, collect the number status codes, and evaluate the data against a previously trained RCF model. Persist the source and results as a time series to Amazon DynamoDB.
• D. Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or 1 MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a 1-minute window using the RCF function and summarize the count of status codes. Persist the results to Amazon S3 through a Kinesis Data Analytics output to an AWS Lambda integration.

Answer: B

NEW QUESTION # 32
A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?

• A. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.
• B. Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.
• C. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.
• D. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script. Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.

Answer: B

NEW QUESTION # 33
A real estate company has a mission-critical application using Apache HBase in Amazon EMR. Amazon EMR is configured with a single master node. The company has over 5 TB of data stored on an Hadoop Distributed File System (HDFS). The company wants a cost-effective solution to make its HBase data highly available.
Which architectural pattern meets company's requirements?

• A. Store the data on an EMR File System (EMRFS) instead of HDFS. Enable EMRFS consistent view.
  Create an EMR HBase cluster with multiple master nodes. Point the HBase root directory to an Amazon S3 bucket.
• B. Use Spot Instances for core and task nodes and a Reserved Instance for the EMR master node.
  Configure
  the EMR cluster with multiple master nodes. Schedule automated snapshots using Amazon EventBridge.
• C. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
  Run two separate EMR clusters in two different Availability Zones. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.
• D. Store the data on an EMR File System (EMRFS) instead of HDFS and enable EMRFS consistent view.
  Create a primary EMR HBase cluster with multiple master nodes. Create a secondary EMR HBase read- replica cluster in a separate Availability Zone. Point both clusters to the same HBase root directory in the same Amazon S3 bucket.

Answer: D

NEW QUESTION # 34
A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project.
Which solution meets these requirements?

• A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.
• B. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.
• C. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.
• D. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.

Answer: D

NEW QUESTION # 35
A data engineer is using AWS Glue ETL jobs to process data at frequent intervals The processed data is then copied into Amazon S3 The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job Which solution will meet these requirements MOST cost-effectively?

• A. Use the AWS Glue Data Catalog to manage the data catalog Define an AWS Glue workflow for the ETL process Define a trigger within the workflow that can start the crawler when an ETL job run is complete
• B. Use the AWS Glue Data Catalog to manage the data catalog Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.
• C. Use the AWS Glue Data Catalog to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.
• D. Use an Apache Hive metastore to manage the data catalog Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

Answer: A

NEW QUESTION # 36
A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.
Which solution will meet these requirements?

• A. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
• B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
• C. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
• D. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

Answer: C

NEW QUESTION # 37
A media company is using Amazon QuickSight dashboards to visualize its national sales dat a. The dashboard is using a dataset with these fields: ID, date, time_zone, city, state, country, longitude, latitude, sales_volume, and number_of_items.
To modify ongoing campaigns, the company wants an interactive and intuitive visualization of which states across the country recorded a significantly lower sales volume compared to the national average.
Which addition to the company's QuickSight dashboard will meet this requirement?

• A. A geospatial color-coded chart of sales volume data across the country.
• B. A pivot table of sales volume data summed up at the state level.
• C. A drill through to other dashboards containing state-level sales volume data.
• D. A drill-down layer for state-level sales volume data.

Answer: B

NEW QUESTION # 38
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

• A. In compressed .csv partitioned by date and sorted by source IP
• B. In Apache ORC partitioned by date and sorted by source IP
• C. In Apache Parquet partitioned by source IP and sorted by date
• D. In compressed nested JSON partitioned by source IP and sorted by date

Answer: B

NEW QUESTION # 39
A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.
Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners' dashboards?

• A. Send the reference data to an Amazon Kinesis Data Firehose delivery stream. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.
• B. Create and schedule an AWS Glue Spark job to run every 5 minutes. The job inserts reference data into Amazon Redshift.
• C. Send reference data to Amazon Kinesis Data Streams. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.
• D. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.

Answer: D

NEW QUESTION # 40
A company using Amazon QuickSight Enterprise edition has thousands of dashboards analyses and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.
Which solution should the company implement to simplify permissions management?

• A. Use QuickSight folders to organize dashboards, analyses, and datasets Assign individual users permissions to these folders
• B. Use AWS 1AM resource-based policies to assign group permissions to QuickSight items
• C. Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions
• D. Use QuickSight folders to organize dashboards analyses, and datasets Assign group permissions by using these folders.

Answer: B

NEW QUESTION # 41
A large retailer has successfully migrated to an Amazon S3 data lake architecture. The company's marketing team is using Amazon Redshift and Amazon QuickSight to analyze data, and derive and visualize insights. To ensure the marketing team has the most up-to-date actionable information, a data analyst implements nightly refreshes of Amazon Redshift using terabytes of updates from the previous day.
After the first nightly refresh, users report that half of the most popular dashboards that had been running correctly before the refresh are now running much slower. Amazon CloudWatch does not show any alerts.
What is the MOST likely cause for the performance degradation?

• A. The nightly data refreshes left the dashboard tables in need of a vacuum operation that could not be automatically performed by Amazon Redshift due to ongoing user workloads.
• B. The cluster is undersized for the queries being run by the dashboards.
• C. The dashboards are suffering from inefficient SQL queries.
• D. The nightly data refreshes are causing a lingering transaction that cannot be automatically closed by Amazon Redshift due to ongoing user workloads.

Answer: A

Explanation:
Explanation
https://github.com/awsdocs/amazon-redshift-developer-guide/issues/21

NEW QUESTION # 42
A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website clickstream data. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day's worth of data in an Amazon ES cluster.
The company has very slow query performance on the Amazon ES index and occasionally sees errors from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10 nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon EBS storage attached and the cluster is configured with 1,000 shards. Occasionally, JVMMemoryPressure errors are found in the cluster logs.
Which solution will improve the performance of Amazon ES?

• A. Decrease the number of Amazon ES data nodes.
• B. Decrease the number of Amazon ES shards for the index.
• C. Increase the number of Amazon ES shards for the index.
• D. Increase the memory of the Amazon ES master nodes.

Answer: B

Explanation:
Explanation
https://aws.amazon.com/premiumsupport/knowledge-center/high-jvm-memory-pressure-elasticsearch/

NEW QUESTION # 43
A regional energy company collects voltage data from sensors attached to buildings. To address any known dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are delivered as quickly as possible. The system must be fully managed and highly available. The company also needs a solution that will automatically scale up as it covers additional cites with this monitoring feature. The alerting system is subscribed to an Amazon SNS topic for remediation.
Which solution meets these requirements?

• A. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.
• B. Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming data. Use the Spark Streaming application to detect the known event sequence and send the SNS message.
• C. Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message stream. Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.
• D. Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function.
  Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the Lambda function, store incoming events in the RDS database and query the latest data to detect the known event sequence and send the SNS message.

Answer: C

NEW QUESTION # 44
......

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