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DAS-C01 Exam Dumps - AWS Certified Data Analytics - Specialty

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Question # 17

A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to execute. The company needs to optimize query execution and avoid any downtime.

What is the MOST cost-effective solution?

Enable concurrency scaling in the workload management (WLM) queue.

Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.

Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.

Use a snapshot, restore, and resize operation. Switch to the new target cluster.

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Question # 18

A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly Once a new month begins, no new writes are made to any of the indices from the previous months The company has been expanding the storage on the Amazon OpenSearch Service {Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs Most searches on the cluster are on the most recent 3 months of data while the audit team requires infrequent access to older data to generate periodic reports The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs

Which of the following is the MOST operationally efficient solution to meet these requirements?

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier When the audit team requires the archived data restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3 When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage When the audit team requires the older data: migrate the indices in UltraWarm storage back to hot storage

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Question # 19

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 # 20

A company using Amazon QuickSight Enterprise edition has thousands of dashboards analyses and datasets. The company struggles to manage and assign permissions for granting users access to various items within QuickSight. The company wants to make it easier to implement sharing and permissions management.

Which solution should the company implement to simplify permissions management?

Use QuickSight folders to organize dashboards, analyses, and datasets Assign individual users permissions to these folders

Use QuickSight folders to organize dashboards analyses, and datasets Assign group permissions by using these folders.

Use AWS 1AM resource-based policies to assign group permissions to QuickSight items

Use QuickSight user management APIs to provision group permissions based on dashboard naming conventions

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Question # 21

An analytics team uses Amazon OpenSearch Service for an analytics API to be used by data analysts. The OpenSearch Service cluster is configured with three master nodes. The analytics team uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and a customized data pipeline to ingest and store 2 months of data in an OpenSearch Service cluster. The cluster stopped responding, which is regularly causing timeout requests. The analytics team discovers the cluster is handling too many bulk indexing requests.

Which actions would improve the performance of the OpenSearch Service cluster? (Select TWO.)

Reduce the number of API bulk requests on the OpenSearch Service cluster and reduce the size of each bulk request.

Scale out the OpenSearch Service cluster by increasing the number of nodes.

Reduce the number of API bulk requests on the OpenSearch Service cluster, but increase the size of each bulk request.

Increase the number of master nodes for the OpenSearch Service cluster.

Scale down the pipeline component that is used to ingest the data into the OpenSearch Service cluster.

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Question # 22

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 # 23

A media company is using Amazon QuickSight dashboards to visualize its national sales data. The dashboard is using a dataset with these fields: ID, date, time_zone, city, state, country, longitude, latitude, sales_volume, and number_of_items.

To modify ongoing campaigns, the company wants an interactive and intuitive visualization of which states across the country recorded a significantly lower sales volume compared to the national average.

Which addition to the companyâ€™s QuickSight dashboard will meet this requirement?

A geospatial color-coded chart of sales volume data across the country.

A pivot table of sales volume data summed up at the state level.

A drill-down layer for state-level sales volume data.

A drill through to other dashboards containing state-level sales volume data.

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Question # 24

A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.

Which action would MOST likely increase the performance of accessing log data in Amazon S3?

Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.

Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.

Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.

Redeploy the EMR clusters that are running slowly to a different Availability Zone.

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