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Amazon DAS-C01 Practice Test - Questions Answers, Page 3

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Question 21

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A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesi

Question 22

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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

Question 23

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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 parti

Question 24

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A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owne

Question 25

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A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream. After a successful marketing campaign, the application experienced a signi

Question 26

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A marketing company collects data from third-party providers and uses transient Amazon EMR clusters to process this data. The company wants to host an Apache Hive metastore that is persistent, reli

Question 27

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A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run ana

Question 28

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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,

Question 29

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A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s data. The Amazon Redshift cluster contains a product table and a transactions table,

Question 30

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An energy company collects voltage data in real time from sensors that are attached to buildings. The company wants to receive notifications when a sequence of two voltage drops is detected within 1

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 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 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 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 21

A company wants to improve user satisfaction for its smart home system by adding more features to its recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that, when a sensor is malfunctioning, its recorded data is not always sent to the cloud. The company needs a solution that offers near-real-time analytics on the data from the most updated sensors. Which solution enables the company to meet these requirements?

Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched datato a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.

Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a companydeveloped anomaly detection SQL script.Direct the output of KDA application to a Kinesis Data Firehose delivery stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster.

Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side. Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data transformation feature to flatten theJSON file before sending it to an Amazon S3 bucket. Load the S3 data into an Amazon Redshift cluster.

Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiatean Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.

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Question 22

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 23

Which solution will meet these requirements MOST cost-effectively?

Use the AWS Glue Data Catalog to manage the data catalog. Define an AWS Glue workflow for the ETL process. Define a trigger within the workflow that can start the crawler when an ETL job run is complete.

Use the AWS Glue Data Catalog to manage the data catalog. Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.

Use an Apache Hive metastore to manage the data catalog. Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

Use the AWS Glue Data Catalog to manage the data catalog. Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

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Question 24

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.

More testing facilities were recently added, and the time to process files is increasing. What will MOST efficiently decrease the data processing time?

Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.

Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.

Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly.Process the files in Amazon Redshift.

Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.

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Question 25

A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.

After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredIteratorExceptions error sporadically.

What should the data analyst do to resolve this?

Increase the number of threads that process the stream records.

Increase the provisioned read capacity units assigned to the stream’s Amazon DynamoDB table.

Increase the provisioned write capacity units assigned to the stream’s Amazon DynamoDB table.

Decrease the provisioned write capacity units assigned to the stream’s Amazon DynamoDB table.

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Question 26

A marketing company collects data from third-party providers and uses transient Amazon EMR clusters to process this data.

The company wants to host an Apache Hive metastore that is persistent, reliable, and can be accessed by EMR clusters and multiple AWS services and accounts simultaneously. The metastore must also be available at all times. Which solution meets these requirements with the LEAST operational overhead?

Use AWS Glue Data Catalog as the metastore

Use an external Amazon EC2 instance running MySQL as the metastore

Use Amazon RDS for MySQL as the metastore

Use Amazon S3 as the metastore

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Question 27

A company stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.

Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?

Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.

Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.

Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.

Use AWS WAF to block public internet access to the EMR clusters across the board.

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Question 28

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 company’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 29

A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s data.

The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column.

The tables are over 100 GB in size. The majority of queries run on both tables.

Which distribution style should the company use for the two tables to achieve optimal query performance?

An EVEN distribution style for both tables

A KEY distribution style for both tables

An ALL distribution style for the product table and an EVEN distribution style for the transactions table

An EVEN distribution style for the product table and an KEY distribution style for the transactions table

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Question 30

An energy company collects voltage data in real time from sensors that are attached to buildings. The company wants to receive notifications when a sequence of two voltage drops is detected within 10 minutes of a sudden voltage increase at the same building. All notifications must be delivered as quickly as possible. The system must be highly available. The company needs a solution that will automatically scale when this monitoring feature is implemented in other cities.

The notification system is subscribed to an Amazon Simple Notification Service (Amazon SNS) topic for remediation. Which solution will meet these requirements?

Create an Amazon Managed Streaming for Apache Kafka cluster to ingest the data. Use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming data. Usethe Spark Streaming application to detect the known event sequence and send the SNS message.

Create a REST-based web service by using Amazon API Gateway in front of an AWS Lambda function. Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS to meet current demand. Configure the Lambdafunction to store incoming events in the RDS for PostgreSQL database, query the latest data to detect the known event sequence, and send the SNS message.

Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.

Create an Amazon Kinesis data stream to capture the incoming sensor data. Create another stream for notifications. Set up AWS Application Auto Scaling on both streams. Create an Amazon Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message stream Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.

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