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 Amazon FSx for Windows File Server. Set the Active Directory domain for authentication.

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.

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.

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.

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.

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 crawler to catalog the data.

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 Redshift data source to read the native Amazon Redshift tables.

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.

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 to an Amazon Kinesis data stream to deliver the data 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 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.

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.

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?

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

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.

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?

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.

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.

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?

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 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.

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 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 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.

A company wants to use automatic machine learning (ML) to create and visualize forecasts of complex scenarios and trends.Which solution will meet these requirements with the LEAST management overhead?

Use Amazon QuickSight to visualize the data. Use ML-powered forecasting in QuickSight to create forecasts.

Use an AWS Glue ML job to transform the data and create forecasts. Use Amazon QuickSight to visualize the data.

Use Amazon QuickSight to visualize the data. Use ML-powered forecasting in QuickSight to create forecasts.

Use a prebuilt ML AMI from the AWS Marketplace to create forecasts. Use Amazon QuickSight to visualize the data.

Use Amazon SageMaker inference pipelines to create and update forecasts. Use Amazon QuickSight to visualize the combined data.

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?

Invoke an AWS Lambda function from Kinesis Data Firehose to mask the PII before Kinesis Data Firehose delivers the data to the S3 bucket.

Use Amazon Macie to scan the S3 bucket. Configure Macie to discover Pll. Invoke an AWS Lambda function from S3 events to mask the Pll.

Configure server-side encryption (SSE) for the S3 bucket. Invoke an AWS Lambda function from S3 events to mask the PII.

Create an AWS Lambda function to read the objects, mask the Pll, and store the objects back with same key. Invoke the Lambda function from S3 events.

A company has a mobile app that has millions of users. The company wants to enhance the mobile app by including interactive data visualizations that show user trends.The data for visualization is stored in a large data lake with 50 million rows. Data that is used in the visualization should be no more than two hours old.Which solution will meet these requirements with the LEAST operational overhead?

Run an hourly batch process that renders user-specific data visualizations as static images that are stored in Amazon S3.

Precompute aggregated data hourly. Store the data in Amazon DynamoDB. Render the data by using the D3.js JavaScript library.

Embed an Amazon QuickSight Enterprise edition dashboard into the mobile app by using the QuickSight Embedding SDK. Refresh data in SPICE hourly.

Run Amazon Athena queries behind an Amazon API Gateway API. Render the data by using the D3.js JavaScript library.

A large ecommerce company uses Amazon DynamoDB with provisioned read capacity and auto scaled write capacity to store its product catalog. The company uses Apache HiveQL statements on an Amazon EMR cluster to query the DynamoDB table. After the company announced a sale on all of its products, wait times for each query have increased. The data analyst has determined that the longer wait times are being caused by throttling when querying the table.Which solution will solve this issue?

Increase the DynamoDB table's provisioned read throughput.

Increase the size of the EMR nodes that are provisioned.

Increase the number of EMR nodes that are in the cluster.

Increase the DynamoDB table's provisioned write throughput.

Increase the DynamoDB table's provisioned read throughput.

Amazon DAS-C01 Practice Test 6 - Free Online Exam Questions 201-214

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?

Become a Premium Member for full access

Unlock Premium Member

Amazon DAS-C01 Practice Test 6

Question

Amazon DAS-C01 Practice Tests

Practice Tests