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Amazon DAS-C01 Practice Test - Questions Answers, Page 17

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Question 161

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A company stores revenue data in Amazon Redshift. A data analyst needs to create a dashboard so that the company’s sales team can visualize historical revenue and accurately forecast revenue for the

Question 162

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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

Question 163

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An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnection

Question 164

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A company recently created a test AWS account to use for a development environment. The company also created a production AWS account in another AWS Region. As part of its security testing, the comp

Question 165

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A social media company is using business intelligence tools to analyze data for forecasting. The company is using Apache Kafka to ingest data. The company wants to build dynamic dashboards that incl

Question 166

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A large media company is looking for a cost-effective storage and analysis solution for its daily media recordings formatted with embedded metadata. Daily data sizes range between 10-12 TB with stre

Question 167

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A company hosts its analytics solution on premises. The analytics solution includes a server that collects log files. The analytics solution uses an Apache Hadoop cluster to analyze the log files ho

Question 168

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A company collects data from parking garages. Analysts have requested the ability to run reports in near real time about the number of vehicles in each garage. The company wants to build an ingesti

Question 169

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A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket. The financial statements must not be modified or deleted after they are saved to the S3 bucket. Which soluti

Question 170

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An event ticketing website has a data lake on Amazon S3 and a data warehouse on Amazon Redshift. Two datasets exist: events data and sales data. Each dataset has millions of records. The entire eve

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 161

Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.

Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into AmazonRedshift.Add the new forecast information to the dashboard.

Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget Publish the analysis as a dashboard.

Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.

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Question 162

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?

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.

Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.

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.

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.

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Question 163

An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query. Which steps will create the required logs?

Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.

Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.

Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.

Enable and download audit reports from AWS Artifact.

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Question 164

A company recently created a test AWS account to use for a development environment. The company also created a production AWS account in another AWS Region. As part of its security testing, the company wants to send log data from Amazon CloudWatch Logs in its production account to an Amazon Kinesis data stream in its test account. Which solution will allow the company to accomplish this goal?

Create a subscription filter in the production account’s CloudWatch Logs to target the Kinesis data stream in the test account as its destination. In the test account, create an IAM role that grants access to the Kinesis data stream and theCloudWatch Logs resources in the production account.

In the test account, create an IAM role that grants access to the Kinesis data stream and the CloudWatch Logs resources in the production account. Create a destination data stream in Kinesis Data Streams in the test account with anIAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account.

Create a destination data stream in Kinesis Data Streams in the test account with an IAM role and a trust policy that allow CloudWatch Logs in the production account to write to the test account. Create a subscription filter in theproduction account’s CloudWatch Logs to target the Kinesis data stream in the test account as its destination.

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Question 165

A social media company is using business intelligence tools to analyze data for forecasting. The company is using Apache Kafka to ingest data. The company wants to build dynamic dashboards that include machine learning (ML) insights to forecast key business trends.

The dashboards must show recent batched data that is not more than 75 minutes old. Various teams at the company want to view the dashboards by using Amazon QuickSight with ML insights.

Which solution will meet these requirements?

Replace Kafka with Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

Replace Kafka with an Amazon Kinesis data stream. Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Standard edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Refresh the data in QuickSight Standard edition SPICE from Amazon S3 by using a scheduled AWS Lambda function. Configure the Lambda function to run every 75 minutes and to invoke the QuickSight API to create a dynamic dashboard that includes forecasting and ML insights.

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Question 166

A large media company is looking for a cost-effective storage and analysis solution for its daily media recordings formatted with embedded metadata. Daily data sizes range between 10-12 TB with stream analysis required on timestamps, video resolutions, file sizes, closed captioning, audio languages, and more. Based on the analysis,

processing the datasets is estimated to take between 30-180 minutes depending on the underlying framework selection. The analysis will be done by using business intelligence (Bl) tools that can be connected to data sources with AWS or Java Database Connectivity (JDBC) connectors.

Which solution meets these requirements?

Store the video files in Amazon DynamoDB and use AWS Lambda to extract the metadata from the files and load it to DynamoDB. Use DynamoDB to provide the data to be analyzed by the Bl tools.

Store the video files in Amazon S3 and use AWS Lambda to extract the metadata from the files and load it to Amazon S3. Use Amazon Athena to provide the data to be analyzed by the BI tools.

Store the video files in Amazon DynamoDB and use Amazon EMR to extract the metadata from the files and load it to Apache Hive. Use Apache Hive to provide the data to be analyzed by the Bl tools.

Store the video files in Amazon S3 and use AWS Glue to extract the metadata from the files and load it to Amazon Redshift. Use Amazon Redshift to provide the data to be analyzed by the Bl tools.

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Question 167

A company hosts its analytics solution on premises. The analytics solution includes a server that collects log files. The analytics solution uses an Apache Hadoop cluster to analyze the log files hourly and to produce output files. All the files are archived to another server for a specified duration.

The company is expanding globally and plans to move the analytics solution to multiple AWS Regions in the AWS Cloud. The company must adhere to the data archival and retention requirements of each country where the data is stored.

Which solution will meet these requirements?

Create an Amazon S3 bucket in one Region to collect the log files. Use S3 event notifications to invoke an AWS Glue job for log analysis. Store the output files in the target S3 bucket. Use S3 Lifecycle rules on the target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

Create a Hadoop Distributed File System (HDFS) file system on an Amazon EMR cluster in one Region to collect the log files. Set up a bootstrap action on the EMR cluster to run an Apache Spark job. Store the output files in a target Amazon S3 bucket. Schedule a job on one of the EMR nodes to delete files that no longer need to be retained.

Create an Amazon S3 bucket in each Region to collect log files. Create an Amazon EMR cluster. Submit steps on the EMR cluster for analysis. Store the output files in a target S3 bucket in each Region. Use S3 Lifecycle rules on each target S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

Create an Amazon Kinesis Data Firehose delivery stream in each Region to collect log data. Specify an Amazon S3 bucket in each Region as the destination. Use S3 Storage Lens for data analysis. Use S3 Lifecycle rules on each destination S3 bucket to set an expiration period that meets the retention requirements of the country that contains the Region.

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Question 168

A company collects data from parking garages. Analysts have requested the ability to run reports in near real time about the number of vehicles in each garage.

The company wants to build an ingestion pipeline that loads the data into an Amazon Redshift cluster. The solution must alert operations personnel when the number of vehicles in a particular garage exceeds a specific threshold. The alerting query will use garage threshold values as a static reference. The threshold values are stored in

Amazon S3.

What is the MOST operationally efficient solution that meets these requirements?

Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

Use an Amazon Kinesis data stream to collect the data. Use an Amazon Kinesis Data Firehose delivery stream to deliver the data to Amazon Redshift. Create another Kinesis data stream to temporarily store the threshold values from Amazon S3. Send the delivery stream and the second data stream to Amazon Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value. Configure an AWS Lambda function to publish an C. Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Automatically initiate an AWS Lambda function that queries the data in Amazon Redshift. Configure the Lambda function to compare the number of vehicles in a particular garage to the corresponding threshold value from Amazon S3. Configure the Lambda function to also publish an Amazon Simple Notification Service (Amazon SNS) notification if the number of vehicles exceeds the threshold.

Use an Amazon Kinesis Data Firehose delivery stream to collect the data and to deliver the data to Amazon Redshift. Create an Amazon Kinesis Data Analytics application that uses the same delivery stream as an input source. Use Kinesis Data Analytics to compare the number of vehicles in a particular garage to the corresponding threshold value that is stored in a table as an in-application stream. Configure an AWS Lambda function as an output for the application to publish an Amazon Simple Queue Service (Amazon SQS) notification if the number of vehicles exceeds the threshold.

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Question 169

A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket. The financial statements must not be modified or deleted after they are saved to the S3 bucket.

Which solution will meet these requirements?

Create the S3 bucket with S3 Object Lock in governance mode.

Create the S3 bucket with MFA delete enabled.

Create the S3 bucket with S3 Object Lock in compliance mode.

Create S3 buckets in two AWS Regions. Use S3 Cross-Region Replication (CRR) between the buckets.

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Question 170

An event ticketing website has a data lake on Amazon S3 and a data warehouse on Amazon Redshift. Two datasets exist: events data and sales data. Each dataset has millions of records.

The entire events dataset is frequently accessed and is stored in Amazon Redshift. However, only the last 6 months of sales data is frequently accessed and is stored in Amazon Redshift. The rest of the sales data is available only in Amazon S3.

A data analytics specialist must create a report that shows the total revenue that each event has generated in the last 12 months. The report will be accessed thousands of times each week.

Which solution will meet these requirements with the LEAST operational effort?

Create an AWS Glue job to access sales data that is older than 6 months from Amazon S3 and to access event and sales data from Amazon Redshift. Load the results into a new table in Amazon Redshift.

Create a stored procedure to copy sales data that is older than 6 months and newer than 12 months from Amazon S3 to Amazon Redshift. Create a materialized view with the autorefresh option

Create an AWS Lambda function to copy sales data that is older than 6 months and newer than 12 months to an Amazon Kinesis Data Firehose delivery stream. Specify Amazon Redshift as the destination of the delivery stream. Create a materialized view with the autorefresh option.

Create a materialized view in Amazon Redshift with the autorefresh option. Use Amazon Redshift Spectrum to include sales data that is older than 6 months.

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