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Amazon DAS-C01 Practice Test - Questions Answers, Page 18

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Question 171

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A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data s

Question 172

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A financial company uses Amazon Athena to query data from an Amazon S3 data lake. Files are stored in the S3 data lake in Apache ORC format. Data analysts recently introduced nested fields in the da

Question 173

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A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehos

Question 174

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A company uses Amazon Connect to manage its contact center. The company uses Salesforce to manage its customer relationship management (CRM) data. The company must build a pipeline to ingest data fr

Question 175

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A company is designing a data warehouse to support business intelligence reporting. Users will access the executive dashboard heavily each Monday and Friday morning for I hour. These read-only queri

Question 176

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A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes

Question 177

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A company is creating a data lake by using AWS Lake Formation. The data that will be stored in the data lake contains sensitive customer information and must be encrypted at rest using an AWS Key Ma

Question 178

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An ecommerce company uses Amazon Aurora PostgreSQL to process and store live transactional data and uses Amazon Redshift for its data warehouse solution. A nightly ET L job has been implemented to u

Question 179

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A large marketing company needs to store all of its streaming logs and create near-real-time dashboards. The dashboards will be used to help the company make critical business decisions and must be

Question 180

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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 th

Related questions

A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals. The company wants to run an additional analysis on a year’s worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week. What is the MOST cost-effective solution?

A bank is building an Amazon S3 data lake. The bank wants a single data repository for customer data needs, such as personalized recommendations. The bank needs to use Amazon Kinesis Data Firehose to ingest customers' personal information, bank accounts, and transactions in near real time from a transactional relational database. All personally identifiable information (Pll) that is stored in the S3 bucket must be masked. The bank has enabled versioning for the S3 bucket. Which solution will meet these requirements?

A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day. A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data. How can the data analyst meet these requirements with the lowest maintenance overhead?

A company is building a data lake and needs to ingest data from a relational database that has time-series data. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3. What is the MOST cost-effective approach to meet these requirements?

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 company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to be 50 GB each day. The company is using Amazon Athena to query the raw data in Amazon S3 directly. Most queries aggregate data from the past 12 months, and data that is older than 5 years is infrequently queried. The typical query scans about 500 MB of data and is expected to return results in less than 1 minute. The raw data must be retained indefinitely for compliance requirements. Which solution meets the company’s requirements?

A company wants to use a data lake that is hosted on Amazon S3 to provide analytics services for historical data. The data lake consists of 800 tables but is expected to grow to thousands of tables. More than 50 departments use the tables, and each department has hundreds of users. Different departments need access to specific tables and columns. Which solution will meet these requirements with the LEAST operational overhead?

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 company uses Amazon Redshift for its data warehouse. The company is running an ET L process that receives data in data parts from five third-party providers. The data parts contain independent records that are related to one specific job. The company receives the data parts at various times throughout each day. A data analytics specialist must implement a solution that loads the data into Amazon Redshift only after the company receives all five data parts. Which solution will meet these requirements?

A company needs to implement a near-real-time messaging system for hotel inventory. The messages are collected from 1,000 data sources and contain hotel inventory data. The data is then processed and distributed to 20 HTTP endpoint destinations. The range of data size for messages is 2-500 KB. The messages must be delivered to each destination in order. The performance of a single destination HTTP endpointshould not impact the performance of the delivery for other destinations. Which solution meets these requirements with the LOWEST latency from message ingestion to delivery?

Question 171

A machinery company wants to collect data from sensors. A data analytics specialist needs to implement a solution that aggregates the data in near-real time and saves the data to a persistent data store. The data must be stored in nested JSON format and must be queried from the data store with a latency of single-digit milliseconds.

Which solution will meet these requirements?

Use Amazon Kinesis Data Streams to receive the data from the sensors. Use Amazon Kinesis Data Analytics to read the stream, aggregate the data, and send the data to an AWS Lambda function. Configure the Lambda function to store the data in Amazon DynamoDB.

Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read the data from Kinesis Data Analytics and store the data in Amazon S3.

Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data from Kinesis Data Firehose in Amazon DynamoDB.

Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWS Lambda function to aggregate the data during capture. Store the data in Amazon S3.

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Question 172

A financial company uses Amazon Athena to query data from an Amazon S3 data lake. Files are stored in the S3 data lake in Apache ORC format. Data analysts recently introduced nested fields in the data lake ORC files, and noticed that queries are taking longer to run in Athena. A data analysts discovered that more data than what is required is being scanned for the queries.

What is the MOST operationally efficient solution to improve query performance?

Flatten nested data and create separate files for each nested dataset.

Use the Athena query engine V2 and push the query filter to the source ORC file.

Use Apache Parquet format instead of ORC format.

Recreate the data partition strategy and further narrow down the data filter criteria.

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Question 173

A gaming company is building a serverless data lake. The company is ingesting streaming data into Amazon Kinesis Data Streams and is writing the data to Amazon S3 through Amazon Kinesis Data Firehose. The company is using 10 MB as the S3 buffer size and is using 90 seconds as the buffer interval. The company runs an AWS Glue ET L job to merge and transform the data to a different format before writing the data back to Amazon S3.

Recently, the company has experienced substantial growth in its data volume. The AWS Glue ETL jobs are frequently showing an OutOfMemoryError error.

Which solutions will resolve this issue without incurring additional costs? (Select TWO.)

Place the small files into one S3 folder. Define one single table for the small S3 files in AWS Glue Data Catalog. Rerun the AWS Glue ET L jobs against this AWS Glue table.

Create an AWS Lambda function to merge small S3 files and invoke them periodically. Run the AWS Glue ETL jobs after successful completion of the Lambda function.

Run the S3DistCp utility in Amazon EMR to merge a large number of small S3 files before running the AWS Glue ETL jobs.

Use the groupFiIes setting in the AWS Glue ET L job to merge small S3 files and rerun AWS Glue E TL jobs.

Update the Kinesis Data Firehose S3 buffer size to 128 MB. Update the buffer interval to 900 seconds.

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Question 174

A company uses Amazon Connect to manage its contact center. The company uses Salesforce to manage its customer relationship management (CRM) data. The company must build a pipeline to ingest data from Amazon Connect and Salesforce into a data lake that is built on Amazon S3.

Which solution will meet this requirement with the LEAST operational overhead?

Use Amazon Kinesis Data Streams to ingest the Amazon Connect data. Use Amazon AppFlow to ingest the Salesforce data.

Use Amazon Kinesis Data Firehose to ingest the Amazon Connect data. Use Amazon Kinesis Data Streams to ingest the Salesforce data.

Use Amazon Kinesis Data Firehose to ingest the Amazon Connect data. Use Amazon AppFlow to ingest the Salesforce data.

Use Amazon AppFlow to ingest the Amazon Connect data. Use Amazon Kinesis Data Firehose to ingest the Salesforce data.

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Question 175

A company is designing a data warehouse to support business intelligence reporting. Users will access the executive dashboard heavily each Monday and Friday morning for I hour. These read-only queries will run on the active Amazon Redshift cluster, which runs on dc2.8xIarge compute nodes 24 hours a day, 7 days a week. There are three queues set up in workload management: Dashboard, ETL, and System. The Amazon Redshift cluster needs to process the queries without wait time.

What is the MOST cost-effective way to ensure that the cluster processes these queries?

Perform a classic resize to place the cluster in read-only mode while adding an additional node to the cluster.

Enable automatic workload management.

Perform an elastic resize to add an additional node to the cluster.

Enable concurrency scaling for the Dashboard workload queue.

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Question 176

A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time.

Which solution meets these requirements?

Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.

Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.

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Question 177

A company is creating a data lake by using AWS Lake Formation. The data that will be stored in the data lake contains sensitive customer information and must be encrypted at rest using an AWS Key Management Service (AWS KMS) customer managed key to meet regulatory requirements.

How can the company store the data in the data lake to meet these requirements?

Store the data in an encrypted Amazon Elastic Block Store (Amazon EBS) volume. Register the Amazon EBS volume with Lake Formation.

Store the data in an Amazon S3 bucket by using server-side encryption with AWS KMS (SSE-KMS). Register the S3 location with Lake Formation.

Encrypt the data on the client side and store the encrypted data in an Amazon S3 bucket. Register the S3 location with Lake Formation.

Store the data in an Amazon S3 Glacier Flexible Retrieval vault bucket. Register the S3 Glacier Flexible Retrieval vault with Lake Formation.

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Question 178

An ecommerce company uses Amazon Aurora PostgreSQL to process and store live transactional data and uses Amazon Redshift for its data warehouse solution. A nightly ET L job has been implemented to update the Redshift cluster with new data from the PostgreSQL database. The business has grown rapidly and so has the size and cost of the Redshift cluster. The company's data analytics team needs to create a solution to archive historical data and only keep the most recent 12 months of data in Amazon

Redshift to reduce costs. Data analysts should also be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data.

Which combination of tasks will meet these requirements? (Select THREE.)

Configure the Amazon Redshift Federated Query feature to query live transactional data in the PostgreSQL database.

Configure Amazon Redshift Spectrum to query live transactional data in the PostgreSQL database.

Schedule a monthly job to copy data older than 12 months to Amazon S3 by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

Schedule a monthly job to copy data older than 12 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Redshift Spectrum to access historical data with S3 Glacier Flexible Retrieval.

Create a late-binding view in Amazon Redshift that combines live, current, and historical data from different sources.

Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

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Question 179

A large marketing company needs to store all of its streaming logs and create near-real-time dashboards. The dashboards will be used to help the company make critical business decisions and must be highly available.

Which solution meets these requirements?

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Create the dashboards by using Amazon QuickSight.

Deploy an Amazon Redshift cluster with at least three nodes in a VPC that spans two Availability Zones. Store the streaming logs and use the Redshift cluster as a source to create the dashboards by using Amazon QuickSight.

Store the streaming logs in Amazon S3 with replication to an S3 bucket in a different Availability Zone. Every time a new log is added in the bucket, invoke an AWS Lambda function to update the dashboards in Amazon QuickSight.

Store the streaming logs in Amazon OpenSearch Service deployed across three Availability Zones and with three dedicated master nodes. Create the dashboards by using OpenSearch Dashboards.

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Question 180

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?

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.

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.

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.

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.

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