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Amazon DAS-C01 Practice Test - Questions Answers, Page 15

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Question 141

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A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which subo

Question 142

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Question 143

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An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to

Question 144

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A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format pa

Question 145

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A company has 10-15 ?? of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a onetime query engine. The company wants to transform the data to optimize query runtime a

Question 146

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A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. T

Question 147

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A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics tea

Question 148

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A team of data scientists plans to analyze market trend data for their company’s new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to ut

Question 149

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A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an addit

Question 150

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A data architect is building an Amazon S3 data lake for a bank. The goal is to provide a single data repository for customer data needs, such as personalized recommendations. The bank uses Amazon Ki

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 141

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which suborganization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.

Which approach can provide the visuals that senior leadership requested with the least amount of effort?

Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.

Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

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Question 142

A retail company’s data analytics team recently created multiple product sales analysis dashboards for the average selling price per product using Amazon QuickSight. The dashboards were created from .csv files uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons, restricting access is a key requirement. The product owners should view only their respective product analysis in the dashboard reports.

Which approach should the data analytics team take to allow product owners to view only their products in the dashboard?

Separate the data by product and use S3 bucket policies for authorization.

Separate the data by product and use IAM policies for authorization.

Create a manifest file with row-level security.

Create dataset rules with row-level security.

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Question 143

An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console:

“Command Failed with Exit Code 1.”

Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90–95% soon after. The average memory usage across all executors continues to be less than 4%.

The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.

What should the data engineer do to solve the failure in the MOST cost-effective way?

Change the worker type from Standard to G.2X.

Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.

Increase the fetch size setting by using AWS Glue dynamics frame.

Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

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Question 144

A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.

When defining tables in the Data Catalog, the company has the following requirements:

Choose the catalog table name and do not rely on the catalog table naming algorithm.

Keep the table updated with new partitions loaded in the respective S3 bucket prefixes. Which solution meets these requirements with minimal effort?

Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.

Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.

Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.

Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.

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Question 145

A company has 10-15 ?? of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a onetime query engine. The company wants to transform the data to optimize query runtime and storage costs. Which option for data format and compression meets these requirements?

CSV compressed with zip

JSON compressed with bzip2

Apache Parquet compressed with Snappy

Apache Avro compressed with LZO

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Question 146

A manufacturing company uses Amazon S3 to store its data. The company wants to use AWS Lake Formation to provide granular-level security on those data assets. The data is in Apache Parquet format. The company has set a deadline for a consultant to build a data lake.

How should the consultant create the MOST cost-effective solution that meets these requirements?

Run Lake Formation blueprints to move the data to Lake Formation. Once Lake Formation has the data, apply permissions on Lake Formation.

To create the data catalog, run an AWS Glue crawler on the existing Parquet data. Register the Amazon S3 path and then apply permissions through Lake Formation to provide granular-level security.

Install Apache Ranger on an Amazon EC2 instance and integrate with Amazon EMR. Using Ranger policies, create rolebased access control for the existing data assets in Amazon S3.

Create multiple IAM roles for different users and groups. Assign IAM roles to different data assets in Amazon S3 to create table-based and column-based access controls.

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Question 147

A retail company leverages Amazon Athena for ad-hoc queries against an AWS Glue Data Catalog. The data analytics team manages the data catalog and data access for the company. The data analytics team wants to separate queries and manage the cost of running those queries by different workloads and teams. Ideally, the data analysts want to group the queries run by different users within a team, store the query results in individual Amazon S3 buckets specific to each team, and enforce cost constraints on the queries run against the Data Catalog. Which solution meets these requirements?

Create IAM groups and resource tags for each team within the company. Set up IAM policies that control user access and actions on the Data Catalog resources.

Create Athena resource groups for each team within the company and assign users to these groups. Add S3 bucket names and other query configurations to the properties list for the resource groups.

Create Athena workgroups for each team within the company. Set up IAM workgroup policies that control user access and actions on the workgroup resources.

Create Athena query groups for each team within the company and assign users to the groups.

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Question 148

The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical reprocessing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution. Which solution meets these requirements?

Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream topersist data to an S3 bucket.

Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis Data Firehose onthe Kinesis data stream to persist data to an S3 bucket.

Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS. Configure Kinesis DataFirehose on the second Kinesis data stream to persist data to an S3 bucket.

Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on thesecond Kinesis data stream to persist data to an S3 bucket.

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Question 149

A company analyzes its data in an Amazon Redshift data warehouse, which currently has a cluster of three dense storage nodes. Due to a recent business acquisition, the company needs to load an additional 4 TB of user data into Amazon Redshift. The engineering team will combine all the user data and apply complex calculations that require I/O intensive resources. The company needs to adjust the cluster's capacity to support the change in analytical and storage requirements.

Which solution meets these requirements?

Resize the cluster using elastic resize with dense compute nodes.

Resize the cluster using classic resize with dense compute nodes.

Resize the cluster using elastic resize with dense storage nodes.

Resize the cluster using classic resize with dense storage nodes.

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Question 150

A data architect is building an Amazon S3 data lake for a bank. The goal is to provide a single data repository for customer data needs, such as personalized recommendations. The bank uses Amazon Kinesis Data Firehose to ingest customers’ personal information bank accounts, and transactions in near-real time from a transactional relational database. The bank requires all personally identifiable information (PII) that is stored in the AWS Cloud to be masked. Which solution will meet these requirements?

Invoke an AWS Lambda function from Kinesis Data Firehose to mask PII before delivering the data into Amazon S3.

Use Amazon Made, and configure it to discover and mask PII.

Enable server-side encryption (SSE) in Amazon S3.

Invoke Amazon Comprehend from Kinesis Data Firehose to detect and mask PII before delivering the data into Amazon S3.

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