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Amazon DAS-C01 Practice Test - Questions Answers, Page 19

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Question 181

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A company has multiple data workflows to ingest data from its operational databases into its data lake on Amazon S3. The workflows use AWS Glue and Amazon EMR for data processing and ETL. The compan

Question 182

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A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's data analysts are using

Question 183

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A business intelligence (Bl) engineer must create a dashboard to visualize how often certain keywords are used in relation to others in social media posts about a public figure. The Bl engineer extr

Question 184

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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 rec

Question 185

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A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence r

Question 186

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A company developed a new voting results reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a solution to p

Question 187

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A data analyst notices the following error message while loading data to an Amazon Redshift cluster: 'The bucket you are attempting to access must be addressed using the specified endpoint.' What

Question 188

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A company has a fitness tracker application that generates data from subscribers. The company needs real-time reporting on this data. The data is sent immediately, and the processing latency must be

Question 189

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A large company has several independent business units. Each business unit is responsible for its own data, but needs to share data with other units for collaboration. Each unit stores data in an A

Question 190

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A company collects and transforms data files from third-party providers by using an on-premises SFTP server. The company uses a Python script to transform the dat a. The company wants to reduce the

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 181

A company has multiple data workflows to ingest data from its operational databases into its data lake on Amazon S3. The workflows use AWS Glue and Amazon EMR for data processing and ETL. The company wants to enhance its architecture to provide automated orchestration and minimize manual intervention Which solution should the company use to manage the data workflows to meet these requirements?

AWS Glue workflows

AWS Step Functions

AWS Lambda

AWS Batch

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Question 182

A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's data analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data.

The amount of data that is ingested into Amazon S3 has increased to 5 PB over time. The query latency also has increased. The company needs to segment the data to reduce the amount of data that is scanned.

Which solutions will improve query performance? (Select TWO.)

Use MySQL Workbench on an Amazon EC2 instance. Connect to Athena by using a JDBC connector. Run the query from MySQL Workbench instead of Athena directly.

Configure Athena to use S3 Select to load only the files of the data subset.

Create the data subset in Apache Parquet format each day by using the Athena CREATE TABLE AS SELECT (CTAS) statement. Query the Parquet data.

Run a daily AWS Glue ETL job to convert the data files to Apache Parquet format and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data each day.

Create an S3 gateway endpoint. Configure VPC routing to access Amazon S3 through the gateway endpoint.

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Question 183

A business intelligence (Bl) engineer must create a dashboard to visualize how often certain keywords are used in relation to others in social media posts about a public figure. The Bl engineer extracts the keywords from the posts and loads them into an Amazon Redshift table. The table displays the keywords and the count corresponding to each keyword.

The Bl engineer needs to display the top keywords with more emphasis on the most frequently used keywords.

Which visual type in Amazon QuickSight meets these requirements?

Bar charts

Word clouds

Circle packing

Heat maps

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Question 184

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?

Create an Amazon S3 bucket to receive the data. Use S3 multipart upload to collect the data from the different sources and to form a single object before loading the data into Amazon Redshift.

Use an AWS Lambda function that is scheduled by cron to load the data into a temporary table in Amazon Redshift. Use Amazon Redshift database triggers to consolidate the final data when all five data parts are ready.

Create an Amazon S3 bucket to receive the data. Create an AWS Lambda function that is invoked by S3 upload events. Configure the function to validate that all five data parts are gathered before the function loads the data into Amazon Redshift.

Create an Amazon Kinesis Data Firehose delivery stream. Program a Python condition that will invoke a buffer flush when all five data parts are received.

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Question 185

A financial services company is building a data lake solution on Amazon S3. The company plans to use analytics offerings from AWS to meet user needs for one-time querying and business intelligence reports. A portion of the columns will contain personally identifiable information (Pll). Only authorized users should be able to see plaintext PII data.

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

Define a bucket policy for each S3 bucket of the data lake to allow access to users who have authorization to see PII data. Catalog the data by using AWS Glue. Create two IAM roles. Attach a permissions policy with access to PII columns to one role. Attach a policy without these permissions to the other role.

Register the S3 locations with AWS Lake Formation. Create two IAM roles. Use Lake Formation data permissions to grant Select permissions to all of the columns for one role. Grant Select permissions to only columns that contain non-PII data for the other role.

Register the S3 locations with AWS Lake Formation. Create an AWS Glue job to create an E TL workflow that removes the Pll columns from the data and creates a separate copy of the data in another data lake S3 bucket. Register the new S3 locations with Lake Formation. Grant users the permissions to each data lake data based on whether the users are authorized to see PII data.

Register the S3 locations with AWS Lake Formation. Create two IAM roles. Attach a permissions policy with access to Pll columns to one role. Attach a policy without these permissions to the other role. For each downstream analytics service, use its native security functionality and the IAM roles to secure the Pll data.

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Question 186

A company developed a new voting results reporting website that uses Amazon Kinesis Data Firehose to deliver full logs from AWS WAF to an Amazon S3 bucket. The company is now seeking a solution to perform this infrequent data analysis with data visualization capabilities in a way that requires minimal development effort.

Which solution MOST cost-effectively meets these requirements?

Use an AWS Glue crawler to create and update a table in the AWS Glue data catalog from the logs. Use Amazon Athena to perform ad-hoc analyses. Develop data visualizations by using Amazon QuickSight.

Configure Kinesis Data Firehose to deliver the logs to an Amazon OpenSearch Service cluster. Use OpenSearch Service REST APIs to analyze the data. Visualize the data by building an OpenSearch Service dashboard.

Create an AWS Lambda function to convert the logs to CSV format. Add the Lambda function to the Kinesis Data Firehose transformation configuration. Use Amazon Redshift to perform a one-time analysis of the logs by using SQL queries. Develop data visualizations by using Amazon QuickSight.

Create an Amazon EMR cluster and use Amazon S3 as the data source. Create an Apache Spark job to perform a one-time analysis of the logs. Develop data visualizations by using Amazon QuickSight.

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Question 187

A data analyst notices the following error message while loading data to an Amazon Redshift cluster:

'The bucket you are attempting to access must be addressed using the specified endpoint.'

What should the data analyst do to resolve this issue?

Specify the correct AWS Region for the Amazon S3 bucket by using the REGION option with the COPY command.

Change the Amazon S3 object's ACL to grant the S3 bucket owner full control of the object.

Launch the Redshift cluster in a VPC.

Configure the timeout settings according to the operating system used to connect to the Redshift cluster.

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Question 188

A company has a fitness tracker application that generates data from subscribers. The company needs real-time reporting on this data. The data is sent immediately, and the processing latency must be less than 1 second. The company wants to perform anomaly detection on the data as the data is collected. The company also requires a solution that minimizes operational overhead.

Which solution meets these requirements?

Amazon EMR cluster with Apache Spark streaming, Spark SQL, and Spark's machine learning library (MLIib)

Amazon Kinesis Data Firehose with Amazon S3 and Amazon Athena

Amazon Kinesis Data Firehose with Amazon QuickSight

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics

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Question 189

A large company has several independent business units. Each business unit is responsible for its own data, but needs to share data with other units for collaboration.

Each unit stores data in an Amazon S3 data lake created with AWS Lake Formation. To create dashboard reports, the marketing team wants to join its data stored in an Amazon Redshift cluster with the sales team customer table stored in the data lake. The sales team has a large number of tables and schemas, but the marketing team should only have access to the customer table. The solution must be secure and scalable.

Which set of actions meets these requirements?

The sales team shares the AWS Glue Data Catalog customer table with the marketing team in read-only mode using the named resource method. The marketing team accepts the datashare using AWS Resource Access Manager (AWS RAM) and creates a resource link to the shared customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.

The marketing team creates an S3 cross-account replication between the sales team's S3 bucket as the source and the marketing team's S3 bucket as the destination. The marketing team runs an AWS Glue crawler on the replicated data in its AWS account to create an AWS Glue Data Catalog customer table.

The marketing team joins its data with the customer table using Amazon Redshift Spectrum. The marketing team creates an AWS Lambda function in the sales team's account to replicate data between the sale team's S3 bucket as the source and the marketing team's S3 bucket as the destination. The marketing team runs an AWS Glue crawler on the replicated data in its AWS account to create an AWS Glue Data Catalog customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum

The sales team shares the AWS Glue Data Catalog customer table with the marketing team in read-only mode using the Lake Formation tag-based access control (LF-TBAC) method. The sales team updates the AWS Glue Data Catalog resource policy to add relevant permissions for the marketing team. The marketing team creates a resource link to the shared customer table. The marketing team joins its data with the customer table using Amazon Redshift Spectrum.

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Question 190

A company collects and transforms data files from third-party providers by using an on-premises SFTP server. The company uses a Python script to transform the dat a.

The company wants to reduce the overhead of maintaining the SFTP server and storing large amounts of data on premises. However, the company does not want to change the existing upload process for the third-party providers.

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

Deploy the Python script on an Amazon EC2 instance. Install a third-party SFTP server on the EC2 instance. Schedule the script to run periodically on the EC2 instance to perform a data transformation on new files. Copy the transformed files to Amazon S3.

Create an Amazon S3 bucket that includes a separate prefix for each provider. Provide the S3 URL to each provider for its respective prefix. Instruct the providers to use the S3 COPY command to upload data. Configure an AWS Lambda function that transforms the data when new files are uploaded.

Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Schedule a Python shell job in AWS Glue to use the existing Python script to run periodically and transform the uploaded files.

Use AWS Transfer Family to create an SFTP server that includes a publicly accessible endpoint. Configure the new server to use Amazon S3 storage. Change the server name to match the name of the on-premises SFTP server. Use AWS Data Pipeline to schedule a transient Amazon EMR cluster with an Apache Spark step to periodically transform the files.

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