The Leading Guide To DAS-C01 Practice Test

NEW QUESTION 1
A transportation company uses IoT sensors attached to trucks to collect vehicle data for its global delivery fleet. The company currently sends the sensor data in small .csv files to Amazon S3. The files are then loaded into a 10-node Amazon Redshift cluster with two slices per node and queried using both Amazon Athena and Amazon Redshift. The company wants to optimize the files to reduce the cost of querying and also improve the speed of data loading into the Amazon Redshift cluster.
Which solution meets these requirements?

• A. Use AWS Glue to convert all the files from .csv to a single large Apache Parquet fil
• B. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• C. Use Amazon EMR to convert each .csv file to Apache Avr
• D. COPY the files into Amazon Redshift and query the file with Athena from Amazon S3.
• E. Use AWS Glue to convert the files from .csv to a single large Apache ORC fil
• F. COPY the file into Amazon Redshift and query the file with Athena from Amazon S3.
• G. Use AWS Glue to convert the files from .csv to Apache Parquet to create 20 Parquet file
• H. COPY the files into Amazon Redshift and query the files with Athena from Amazon S3.

Answer: D

NEW QUESTION 2
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?

• A. Use AWS Lambda to group the small files into larger file
• B. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.
• C. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input file
• D. Process the files and load them into Amazon Redshift tables.
• E. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directl
• F. Process the files in Amazon Redshift.
• G. Use Amazon EMR instead of AWS Glue to group the small input file
• H. Process the files in Amazon EMR and load them into Amazon Redshift tables.

Answer: A

NEW QUESTION 3
An education provider’s learning management system (LMS) is hosted in a 100 TB data lake that is built on Amazon S3. The provider’s LMS supports hundreds of schools. The provider wants to build an advanced analytics reporting platform using Amazon Redshift to handle complex queries with optimal performance. System users will query the most recent 4 months of data 95% of the time while 5% of the queries will leverage data from the previous 12 months.
Which solution meets these requirements in the MOST cost-effective way?

• A. Store the most recent 4 months of data in the Amazon Redshift cluste
• B. Use Amazon Redshift Spectrum to query data in the data lak
• C. Use S3 lifecycle management rules to store data from the previous 12 months in Amazon S3 Glacier storage.
• D. Leverage DS2 nodes for the Amazon Redshift cluste
• E. Migrate all data from Amazon S3 to Amazon Redshif
• F. Decommission the data lake.
• G. Store the most recent 4 months of data in the Amazon Redshift cluste
• H. Use Amazon Redshift Spectrum to query data in the data lak
• I. Ensure the S3 Standard storage class is in use with objects in the data lake.
• J. Store the most recent 4 months of data in the Amazon Redshift cluste
• K. Use Amazon Redshift federated queries to join cluster data with the data lake to reduce cost
• L. Ensure the S3 Standard storage class is in use with objects in the data lake.

Answer: C

NEW QUESTION 4
A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.
Which approach is the MOST efficient way to meet these requirements?

• A. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneousl
• B. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold valu
• C. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
• D. Use Amazon Kinesis Data Streams to collect all the data from toll station
• E. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold valu
• F. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not me
• G. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.
• H. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshif
• I. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.
• J. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneousl
• K. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.

Answer: D

NEW QUESTION 5
Once a month, a company receives a 100 MB .csv file compressed with gzip. The file contains 50,000 property listing records and is stored in Amazon S3 Glacier. The company needs its data analyst to query a subset of the data for a specific vendor.
What is the most cost-effective solution?

• A. Load the data into Amazon S3 and query it with Amazon S3 Select.
• B. Query the data from Amazon S3 Glacier directly with Amazon Glacier Select.
• C. Load the data to Amazon S3 and query it with Amazon Athena.
• D. Load the data to Amazon S3 and query it with Amazon Redshift Spectrum.

Answer: A

NEW QUESTION 6
A company’s marketing team has asked for help in identifying a high performing long-term storage service for their data based on the following requirements:
The data size is approximately 32 TB uncompressed.
There is a low volume of single-row inserts each day.
There is a high volume of aggregation queries each day.
Multiple complex joins are performed.
The queries typically involve a small subset of the columns in a table. Which storage service will provide the MOST performant solution?

• A. Amazon Aurora MySQL
• B. Amazon Redshift
• C. Amazon Neptune
• D. Amazon Elasticsearch

Answer: B

NEW QUESTION 7
A hospital is building a research data lake to ingest data from electronic health records (EHR) systems from multiple hospitals and clinics. The EHR systems are independent of each other and do not have a common patient identifier. The data engineering team is not experienced in machine learning (ML) and has been asked to generate a unique patient identifier for the ingested records.
Which solution will accomplish this task?

• A. An AWS Glue ETL job with the FindMatches transform
• B. Amazon Kendra
• C. Amazon SageMaker Ground Truth
• D. An AWS Glue ETL job with the ResolveChoice transform

Answer: A

Explanation:
Matching Records with AWS Lake Formation FindMatches

NEW QUESTION 8
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 utilize Amazon Kinesis to support their use case. 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 re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?

• A. Publish data to one Kinesis data strea
• B. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SN
• C. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• D. Publish data to one Kinesis data strea
• E. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SN
• F. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• G. Publish data to two Kinesis data stream
• H. 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 SN
• I. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• J. Publish data to two Kinesis data stream
• K. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SN
• L. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

Answer: B

NEW QUESTION 9
A company has a business unit uploading .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?

• A. Modify the AWS Glue job to copy the rows into a staging tabl
• B. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.
• C. Load the previously inserted data into a MySQL database in the AWS Glue jo
• D. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.
• E. Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.
• F. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

Answer: A

Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/sql-commands-redshift-glue-job/ See the section Merge an Amazon Redshift table in AWS Glue (upsert)

NEW QUESTION 10
A company hosts an on-premises PostgreSQL database that contains historical data. An internal legacy application uses the database for read-only activities. The company’s business team wants to move the data to a data lake in Amazon S3 as soon as possible and enrich the data for analytics.
The company has set up an AWS Direct Connect connection between its VPC and its on-premises network. A data analytics specialist must design a solution that achieves the business team’s goals with the least operational overhead.
Which solution meets these requirements?

• A. Upload the data from the on-premises PostgreSQL database to Amazon S3 by using a customized batch upload proces
• B. Use the AWS Glue crawler to catalog the data in Amazon S3. Use an AWS Glue job to enrich and store the result in a separate S3 bucket in Apache Parquet forma
• C. Use Amazon Athena to query the data.
• D. Create an Amazon RDS for PostgreSQL database and use AWS Database Migration Service (AWS DMS) to migrate the data into Amazon RD
• E. Use AWS Data Pipeline to copy and enrich the data from the Amazon RDS for PostgreSQL table and move the data to Amazon S3. Use Amazon Athena to querythe data.
• F. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises databas
• G. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet forma
• H. Create an Amazon Redshift cluster and use Amazon Redshift Spectrum to query the data.
• I. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises databas
• J. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet forma
• K. Use Amazon Athena to query the data.

Answer: B

NEW QUESTION 11
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?

• A. Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.
• B. 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.
• C. Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.
• D. Use AWS WAF to block public internet access to the EMR clusters across the board.

Answer: C

Explanation:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-block-public-access.html

NEW QUESTION 12
A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
Station A, which has 10 sensors
Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

• A. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.
• B. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.
• C. Modify the partition key to use the sensor ID instead of the station name.
• D. Reduce the number of sensors in Station A from 10 to 5 sensors.

Answer: C

Explanation:
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding.html
"Splitting increases the number of shards in your stream and therefore increases the data capacity of the stream. Because you are charged on a per-shard basis, splitting increases the cost of your stream"

NEW QUESTION 13
A data analytics specialist is setting up workload management in manual mode for an Amazon Redshift environment. The data analytics specialist is defining query monitoring rules to manage system performance and user experience of an Amazon Redshift cluster.
Which elements must each query monitoring rule include?

• A. A unique rule name, a query runtime condition, and an AWS Lambda function to resubmit any failed queries in off hours
• B. A queue name, a unique rule name, and a predicate-based stop condition
• C. A unique rule name, one to three predicates, and an action
• D. A workload name, a unique rule name, and a query runtime-based condition

Answer: C

NEW QUESTION 14
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

• A. In Apache ORC partitioned by date and sorted by source IP
• B. In compressed .csv partitioned by date and sorted by source IP
• C. In Apache Parquet partitioned by source IP and sorted by date
• D. In compressed nested JSON partitioned by source IP and sorted by date

Answer: A

NEW QUESTION 15
A company has collected more than 100 TB of log files in the last 24 months. The files are stored as raw text in a dedicated Amazon S3 bucket. Each object has a key of the form year-month-day_log_HHmmss.txt where HHmmss represents the time the log file was initially created. A table was created in Amazon Athena that points to the S3 bucket. One-time queries are run against a subset of columns in the table several times an hour.
A data analyst must make changes to reduce the cost of running these queries. Management wants a solution with minimal maintenance overhead.
Which combination of steps should the data analyst take to meet these requirements? (Choose three.)

• A. Convert the log files to Apace Avro format.
• B. Add a key prefix of the form date=year-month-day/ to the S3 objects to partition the data.
• C. Convert the log files to Apache Parquet format.
• D. Add a key prefix of the form year-month-day/ to the S3 objects to partition the data.
• E. Drop and recreate the table with the PARTITIONED BY claus
• F. Run the ALTER TABLE ADD PARTITION statement.
• G. Drop and recreate the table with the PARTITIONED BY claus
• H. Run the MSCK REPAIR TABLE statement.

Answer: BCF

NEW QUESTION 16
A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.
Which combination of steps would meet these requirements? (Choose two.)

• A. Use the COPY command with the manifest file to load data into Amazon Redshift.
• B. Use S3DistCp to load files into Amazon Redshift.
• C. Use temporary staging tables during the loading process.
• D. Use the UNLOAD command to upload data into Amazon Redshift.
• E. Use Amazon Redshift Spectrum to query files from Amazon S3.

Answer: AC

NEW QUESTION 17
An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?

• A. Enable Amazon Redshift Enhanced VPC Routin
• B. Enable VPC Flow Logs to monitor traffic.
• C. Allow access to the Amazon Redshift database using AWS IAM onl
• D. Log access using AWS CloudTrail.
• E. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
• F. Enable and download audit reports from AWS Artifact.

Answer: C

NEW QUESTION 18
A large telecommunications company is planning to set up a data catalog and metadata management for multiple data sources running on AWS. The catalog will be used to maintain the metadata of all the objects stored in the data stores. The data stores are composed of structured sources like Amazon RDS and Amazon Redshift, and semistructured sources like JSON and XML files stored in Amazon S3. The catalog must be updated on a regular basis, be able to detect the changes to object metadata, and require the least possible administration.
Which solution meets these requirements?

• A. Use Amazon Aurora as the data catalo
• B. Create AWS Lambda functions that will connect and gather themetadata information from multiple sources and update the data catalog in Auror
• C. Schedule the Lambda functions periodically.
• D. Use the AWS Glue Data Catalog as the central metadata repositor
• E. Use AWS Glue crawlers to connect to multiple data stores and update the Data Catalog with metadata change
• F. Schedule the crawlers periodically to update the metadata catalog.
• G. Use Amazon DynamoDB as the data catalo
• H. Create AWS Lambda functions that will connect and gather the metadata information from multiple sources and update the DynamoDB catalo
• I. Schedule the Lambda functions periodically.
• J. Use the AWS Glue Data Catalog as the central metadata repositor
• K. Extract the schema for RDS and Amazon Redshift sources and build the Data Catalo
• L. Use AWS crawlers for data stored in Amazon S3 to infer the schema and automatically update the Data Catalog.

Answer: D

NEW QUESTION 19
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