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Amazon DAS-C01 Practice Test - Questions Answers, Page 21

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Question 201

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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 202

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A company's data science team is designing a shared dataset repository on a Windows server. The data repository will store a large amount of training data that the data science team commonly uses in

Question 203

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A financial services firm is processing a stream of real-time data from an application by using Apache Kafka and Kafka MirrorMaker. These tools run on premises and stream data to Amazon Managed Stre

Question 204

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A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored

Question 205

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A network administrator needs to create a dashboard to visualize continuous network patterns over time in a company's AWS account. Currently, the company has VPC Flow Logs enabled and is publishing

Question 206

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A company wants to ingest clickstream data from its website into an Amazon S3 bucket. The streaming data is in JSON format. The data in the S3 bucket must be partitioned by product_id. Which soluti

Question 207

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A company uses an Amazon Redshift provisioned cluster for data analysis. The data is not encrypted at rest. A data analytics specialist must implement a solution to encrypt the data at rest. Which

Question 208

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A company uses Amazon EC2 instances to receive files from external vendors throughout each day. At the end of each day, the EC2 instances combine the files into a single file, perform gzip compressi

Question 209

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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.

Question 210

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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 sum

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 201

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 payload 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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A company's data science team is designing a shared dataset repository on a Windows server. The data repository will store a large amount of training data that the data science team commonly uses in its machine learning models. The data scientists create a random number of new datasets each day.

The company needs a solution that provides persistent, scalable file storage and high levels of throughput and IOPS. The solution also must be highly available and must integrate with Active Directory for access control.

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

Store datasets as files in an Amazon EMR cluster. Set the Active Directory domain for authentication.

Store datasets as files in Amazon FSx for Windows File Server. Set the Active Directory domain for authentication.

Store datasets as tables in a multi-node Amazon Redshift cluster. Set the Active Directory domain for authentication.

Store datasets as global tables in Amazon DynamoDB. Build an application to integrate authentication with the Active Directory domain.

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Question 203

A financial services firm is processing a stream of real-time data from an application by using Apache Kafka and Kafka MirrorMaker. These tools run on premises and stream data to Amazon Managed Streaming for Apache Kafka (Amazon MSK) in the us-east-1 Region. An Apache Flink consumer running on Amazon EMR enriches the data in real time and transfers the output files to an Amazon S3 bucket. The company wants to ensure that the streaming application is highly available across AWS Regions with an RTO of less than 2 minutes.

Which solution meets these requirements?

Launch another Amazon MSK and Apache Flink cluster in the us-west-1 Region that is the same size as the original cluster in the us-east-1 Region. Simultaneously publish and process the data in both Regions. In the event of a disaster that impacts one of the Regions, switch to the other Region.

Add an AWS Lambda function in the us-east-1 Region to read from Amazon MSK and write to a global Amazon

DynamoDB table in on-demand capacity mode. Export the data from DynamoDB to Amazon S3 in the us-west-1 Region. In the event of a disaster that impacts the us-east-1 Region, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region.

Set up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region to the us-west-1 Region. In the event of a disaster, immediately create Amazon MSK and Apache Flink clusters in the us-west-1 Region and start publishing data to this Region. Store 7 days of data in on-premises Kafka clusters and recover the data missed during the recovery time from the on-premises cluster.

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Question 204

A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster.

A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort.

Which combination of steps will create a solution that meets these requirements? (Select THREE.)

Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.

Use an AWS Glue crawler to catalog the data.

Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.

Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.

Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.

Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

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Question 205

A network administrator needs to create a dashboard to visualize continuous network patterns over time in a company's AWS account. Currently, the company has VPC Flow Logs enabled and is publishing this data to Amazon CloudWatch Logs. To troubleshoot networking issues quickly, the dashboard needs to display the new data in near-real time.

Which solution meets these requirements?

Create a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes the data to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

Create an export task from CloudWatch Logs to an Amazon S3 bucket. Create an Amazon QuickSight dashboard to visualize the data.

Create a CloudWatch Logs subscription that uses an AWS Lambda function to stream the CloudWatch Logs data directly into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

Create a CloudWatch Logs subscription to stream CloudWatch Logs data to an AWS Lambda function that writes to an Amazon Kinesis data stream to deliver the data into an Amazon OpenSearch Service cluster. Use OpenSearch Dashboards to create the dashboard.

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Question 206

A company wants to ingest clickstream data from its website into an Amazon S3 bucket. The streaming data is in JSON format. The data in the S3 bucket must be partitioned by product_id.

Which solution will meet these requirements MOST cost-effectively?

Create an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Enable dynamic partitioning. Specify the data field of productjd as one partitioning key.

Create an AWS Glue streaming job to partition the data by productjd before delivering the data to the S3 bucket. Create an Amazon Kinesis Data Firehose delivery stream. Specify the AWS Glue job as the destination of the delivery stream.

Create an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an AWS Glue ETL job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

Create an Amazon Kinesis Data Firehose delivery stream to ingest the streaming data into the S3 bucket. Create an Amazon EMR cluster that includes a job to read the data stream in the S3 bucket, partition the data by productjd, and write the data into another S3 bucket.

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Question 207

A company uses an Amazon Redshift provisioned cluster for data analysis. The data is not encrypted at rest. A data analytics specialist must implement a solution to encrypt the data at rest.

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

Use the ALTER TABLE command with the ENCODE option to update existing columns of the Redshift tables to use LZO encoding.

Export data from the existing Redshift cluster to Amazon S3 by using the UNLOAD command with the ENCRYPTED option. Create a new Redshift cluster with encryption configured. Load data into the new cluster by using the COPY command.

Create a manual snapshot of the existing Redshift cluster. Restore the snapshot into a new Redshift cluster with encryption configured.

Modify the existing Redshift cluster to use AWS Key Management Service (AWS KMS) encryption. Wait for the cluster to finish resizing.

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Question 208

A company uses Amazon EC2 instances to receive files from external vendors throughout each day. At the end of each day, the EC2 instances combine the files into a single file, perform gzip compression, and upload the single file to an Amazon S3 bucket. The total size of all the files is approximately 100 GB each day.

When the files are uploaded to Amazon S3, an AWS Batch job runs a COPY command to load the files into an Amazon Redshift cluster.

Which solution will MOST accelerate the COPY process?

Upload the individual files to Amazon S3. Run the COPY command as soon as the files become available.

Split the files so that the number of files is equal to a multiple of the number of slices in the Redshift cluster. Compress and upload the files to Amazon S3. Run the COPY command on the files.

Split the files so that each file uses 50% of the free storage on each compute node in the Redshift cluster. Compress and upload the files to Amazon S3. Run the COPY command on the files.

pply sharding by breaking up the files so that the DISTKEY columns with the same values go to the same file. Compress and upload the sharded files to Amazon S3. Run the COPY command on the files.

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Question 209

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

Create an 1AM role for each department. Use AWS Lake Formation based access control to grant each 1AM role access to specific tables and columns. Use Amazon Athena to analyze the data.

Create an Amazon Redshift cluster for each department. Use AWS Glue to ingest into the Redshift cluster only the tables and columns that are relevant to that department. Create Redshift database users. Grant the users access to the relevant department's Redshift cluster. Use Amazon Redshift to analyze the data.

Create an 1AM role for each department. Use AWS Lake Formation tag-based access control to grant each 1AM role access to only the relevant resources. Create LF-tags that are attached to tables and columns. Use Amazon Athena to analyze the data.

Create an Amazon EMR cluster for each department. Configure an 1AM service role for each EMR cluster to access

relevant S3 files. For each department's users, create an 1AM role that provides access to the relevant EMR cluster. Use Amazon EMR to analyze the data.

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Question 210

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?

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.

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 Firehose.

Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of I minute or I 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.

Ingest the data stream with Amazon Kinesis Data Firehose with a delivery frequency of 5 minutes or I MB into Amazon S3. Have a Kinesis Data Analytics application evaluate the stream over a I-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.

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