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Amazon DAS-C01 Practice Test - Questions Answers, Page 10

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Question 91

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A media company has a streaming playback application. The company needs to collect and analyze data to provide nearreal- time feedback on playback issues within 30 seconds. The company requires a co

Question 92

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A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error: HIVE_METASTORE_ERROR The error message st

Question 93

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A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications, m

Question 94

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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 an

Question 95

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A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-p

Question 96

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A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has o

Question 97

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A central government organization is collecting events from various internal applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK). The organization has configured a separate Kaf

Question 98

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A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third-party libraries on the clusters by logging in to the master nodes. A data analyst needs to cre

Question 99

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A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, t

Question 100

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A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and ne

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 91

A media company has a streaming playback application. The company needs to collect and analyze data to provide nearreal- time feedback on playback issues within 30 seconds. The company requires a consumer application to identify playback issues, such as decreased quality during a specified time frame. The data will be streamed in JSON format. The schema can change over time. Which solution will meet these requirements?

Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event to invoke an AWS Lambda function to process and analyze the data.

Send the data to Amazon Managed Streaming for Apache Kafka. Configure Amazon Kinesis Data Analytics for SQL Application as the consumer application to process and analyze the data.

Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to initiate an event for AWS Lambda to process and analyze the data.

Send the data to Amazon Kinesis Data Streams. Configure an Amazon Kinesis Data Analytics for Apache Flink application as the consumer application to process and analyze the data.

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Question 92

A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:

HIVE_METASTORE_ERROR

The error message states that the response payload size exceeds the maximum allowed size. The queried table is already partitioned, and the data is stored in an Amazon S3 bucket in the Apache Hive partition format. Which solution will resolve this error?

Modify the Lambda function to upload the query response payload as an object into the S3 bucket. Include an S3 object presigned URL as the payload in the Lambda function response.

Run the MSCK REPAIR TABLE command on the queried table.

Create a separate folder in the S3 bucket. Move the data files that need to be queried into that folder. Create an AWS Glue crawler that points to the folder instead of the S3 bucket.

Check the schema of the queried table for any characters that Athena does not support. Replace any unsupported characters with characters that Athena supports.

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Question 93

A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications, mobile apps, and operational databases.

The company has migrated some of these sources to AWS and now wants to migrate the Flink application. The company must ensure that data that resides in databases within the VPC does not traverse the internet. The application must be able to process all the data that comes from the company’s AWS solution, on-premises resources, and the public internet. Which solution will meet these requirements with the LEAST operational overhead?

Implement Flink on Amazon EC2 within the company’s VP

Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the VPC to collect data that comes from applications and databases within the VPC. UseAmazon Kinesis Data Streams to collect data that comes from the public internet. Configure Flink to have sources from Kinesis Data Streams Amazon MSK, and any onpremises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

Implement Flink on Amazon EC2 within the company’s VP

Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet. Configure Flink to have sources fromKinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink .jar file. Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet.Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink .jar file. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the company’s VPC to collect data that comes fromapplications and databases within the VPC. Use Amazon Kinesis Data Streams to collect data that comes from the public internet. Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams, Amazon MSK, and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

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Question 94

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?

Create an Amazon Kinesis data stream, and ingest the data for each source into the stream. Create 30 AWS Lambda functions to read these messages and send the messages to each destination endpoint.

Create an Amazon Kinesis data stream, and ingest the data for each source into the stream. Create a single enhanced fan-out AWS Lambda function to read these messages and send the messages to each destination endpoint.Register the function as an enhanced fan-out consumer.

Create an Amazon Kinesis Data Firehose delivery stream, and ingest the data for each source into the stream. Configure Kinesis Data Firehose to deliver the data to an Amazon S3 bucket. Invoke an AWS Lambda function with an S3event notification to read these messages and send the messages to each destination endpoint.

Create an Amazon Kinesis data stream, and ingest the data for each source into the stream. Create 20 enhanced fan-out AWS Lambda functions to read these messages and send the messages to each destination endpoint. Register the20 functions as enhanced fan-out consumers.

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Question 95

A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.

How should the data be secured?

Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.

Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.

Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.

Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

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Question 96

A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline. What should the company do to obtain these characteristics?

Design the application so it can remove duplicates during processing be embedding a unique ID in each record.

Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.

Design the data producer so events are not ingested into Kinesis Data Streams multiple times.

Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.

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Question 97

A central government organization is collecting events from various internal applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK). The organization has configured a separate Kafka topic for each application to separate the data. For security reasons, the Kafka cluster has been configured to only allow TLS encrypted data and it encrypts the data at rest.

A recent application update showed that one of the applications was configured incorrectly, resulting in writing data to a Kafka topic that belongs to another application. This resulted in multiple errors in the analytics pipeline as data from different applications appeared on the same topic. After this incident, the organization wants to prevent applications from writing to a topic different than the one they should write to.

Which solution meets these requirements with the least amount of effort?

Create a different Amazon EC2 security group for each application. Configure each security group to have access to a specific topic in the Amazon MSK cluster. Attach the security group to each application based on the topic that theapplications should read and write to.

Install Kafka Connect on each application instance and configure each Kafka Connect instance to write to a specific topic only.

Use Kafka ACLs and configure read and write permissions for each topic. Use the distinguished name of the clients’ TLS certificates as the principal of the ACL.

Create a different Amazon EC2 security group for each application. Create an Amazon MSK cluster and Kafka topic for each application. Configure each security group to have access to the specific cluster.

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Question 98

A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third-party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.

Which options can fulfill these requirements? (Choose two.)

Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.

Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.

Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.

Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with DynamoDB Streams to install the software.

Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance. Create an AMI and use that AMI to create the EMR cluster.

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Question 99

A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies. Which solution meets these requirements?

Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.

Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.

Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.

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Question 100

A company needs to collect streaming data from several sources and store the data in the AWS Cloud. The dataset is heavily structured, but analysts need to perform several complex SQL queries and need consistent performance. Some of the data is queried more frequently than the rest. The company wants a solution that meets its performance requirements in a cost-effective manner. Which solution meets these requirements?

Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon S3. Use Amazon Athena to perform SQL queries over the ingested data.

Use Amazon Managed Streaming for Apache Kafka to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon Redshift. Enable Amazon Redshift workload management (WLM) to prioritize workloads.

Use Amazon Kinesis Data Firehose to ingest the data to save it to Amazon S3. Load frequently queried data to Amazon Redshift using the COPY command. Use Amazon Redshift Spectrum for less frequently queried data.

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