DAS-C01 exam material & Free DAS-C01 AI Tutor

QUESTION: 17

A team of data scientists plans to analyze market trend data for their company's new investment strategy. The trend data comes from ve 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 noti cations based on certain signi cant 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?

Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send noti cations using Amazon SNS. Con gure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and con gure an AWS Lambda function as an output to send noti cations using Amazon SNS. Con gure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the rst stream for analyzing trends, and con gure an AWS Lambda function as an output to send noti cations using Amazon SNS. Con gure Kinesis Data Firehose 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 rst stream for analyzing trends, and send noti cations using Amazon SNS. Con gure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.

Answer(s): B

Explanation:

A concise explanation of the correct choice and why others are incorrect follows.
B) Correct: Kinesis Data Analytics (KDA) provides SQL-like queries over streams, enabling real-time trend analysis; Lambda can output notifications via SNS, and Kinesis Data Firehose can persist to S3 for archival with low operational cost using managed services.
A) Uses a custom KCL app instead of managed KDA for SQL-like queries, increasing maintenance and cost; Firehose on a single stream may suffice, but the architecture isn’t optimized for SQL analytics and serverless outputs.
C) Splits data across streams unnecessarily and uses Firehose on a second stream; Lambda-based output still relies on reactive processing rather than streamlined real-time analysis on a single stream.
D) Uses a custom KCL for analysis and Firehose on the second stream, adding complexity and cost; two streams without leveraging KDA for SQL analytics is suboptimal.

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QUESTION: 18

A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both
Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?

Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.
Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2. Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

Answer(s): B

Explanation:

Athena cross-region querying is supported when the Data Catalog is aware of tables across regions; using a single us-west-2 catalog via a crawler in that region to discover datasets in all Regions minimizes replication and keeps costs low.
A) DMS migration of Glue Data Catalog is unnecessary for cross-region catalog access and adds cost/time.
C) Cross-Region replication of S3 adds storage and transfer costs; duplicating data is not required for cataloging.
D) Granting cross-region catalog access via policy is not a valid mechanism to register and query remote-region data; catalog synchronization is needed.
B) Correct: a single crawler in us-west-2 catalogs datasets in all Regions, enabling centralized queries at minimal cost.

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QUESTION: 19

A large company receives les from external parties in Amazon EC2 throughout the day. At the end of the day, the les are combined into a single le, compressed into a gzip le, and uploaded to Amazon S3. The total size of all the les is close to 100 GB daily. Once the les are uploaded to Amazon S3, an
AWS Batch program executes a COPY command to load the les into an Amazon Redshift cluster.
Which program modi cation will accelerate the COPY process?

Upload the individual les to Amazon S3 and run the COPY command as soon as the les become available.
Split the number of les so they are equal to a multiple of the number of slices in the Amazon Redshift cluster. Gzip and upload the les to Amazon S3. Run the COPY command on the les.
Split the number of les so they are equal to a multiple of the number of compute nodes in the Amazon Redshift cluster. Gzip and upload the les to Amazon S3. Run the COPY command on the les.
Apply sharding by breaking up the les so the distkey columns with the same values go to the same le. Gzip and upload the sharded les to Amazon S3. Run the COPY command on the les.

Answer(s): B

Explanation:

The correct answer is B. Aligning the number of input files to a multiple of Redshift slice count enables optimal parallelism during COPY, reducing bottlenecks and improving throughput. Splitting into equal-sized files and compressing them minimizes I/O and speeds up network transfer to S3 and subsequent loading.
A is incorrect because uploading per-file as they arrive prevents optimal parallelism and can introduce scheduling delays; combining into one big file at the end underutilizes parallel COPY across slices. C is incorrect since using compute node count rather than slice count determines parallelism in distribution, but the COPY parallelism is driven by slices, not nodes. D is incorrect because sharding by distkey value alignment is beneficial for JOIN performance, not for COPY throughput.

Reference:

https://docs.aws.amazon.com/redshift/latest/dg/t_splitting-data- les.html

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QUESTION: 20

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables.
A trips fact table for information on completed rides.
A drivers dimension table for driver pro les.
A customers fact table holding customer pro le information.
The company analyzes trip details by date and destination to examine pro tability by region. The drivers data rarely changes. The customers data frequently changes.
What table design provides optimal query performance?

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables.
Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table.
Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.

Answer(s): C

Explanation:

The optimal design uses a key distribution on trips by destination to colocate data with the relevant dimension and uses a dense, rarely changing drivers dimension with DISTSTYLE ALL to ensure joins are fast; customers, which changes frequently, uses DISTSTYLE EVEN to avoid redistribution overhead on updates.
A) Using DISTSTYLE KEY on trips and ALL on drivers and customers is suboptimal because customers change frequently, and ALL on large dimensions can cause data skew and maintenance overhead.
B) DISTSTYLE EVEN for trips disperses data, hurting joins with destination-based analysis; drivers ALL is ok, but customers needs EVEN to handle frequent updates efficiently.
D) Distinguishing drivers with EVEN and using ALL for both fact tables ignores the benefits of destination-based co-location and proper handling of high-change customer data.

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QUESTION: 21

Three teams of data analysts use Apache Hive on an Amazon EMR cluster with the EMR File System (EMRFS) to query data stored within each teams Amazon
S3 bucket. The EMR cluster has Kerberos enabled and is con gured to authenticate users from the corporate Active Directory. The data is highly sensitive, so access must be limited to the members of each team.
Which steps will satisfy the security requirements?

For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3. Create three additional IAM roles, each granting access to each team's speci c bucket. Add the additional IAM roles to the cluster's EMR role for the EC2 trust policy. Create a security con guration mapping for the additional IAM roles to Active Directory user groups for each team.
For the EMR cluster Amazon EC2 instances, create a service role that grants no access to Amazon S3. Create three additional IAM roles, each granting access to each team's speci c bucket. Add the service role for the EMR cluster EC2 instances to the trust policies for the additional IAM roles. Create a security con guration mapping for the additional IAM roles to Active Directory user groups for each team.
For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3. Create three additional IAM roles, each granting access to each team's speci c bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the additional IAM roles. Create a security con guration mapping for the additional IAM roles to Active Directory user groups for each team.
For the EMR cluster Amazon EC2 instances, create a service role that grants full access to Amazon S3. Create three additional IAM roles, each granting access to each team's speci c bucket. Add the service role for the EMR cluster EC2 instances to the trust polices for the base IAM roles. Create a security con guration mapping for the additional IAM roles to Active Directory user groups for each team.

Answer(s): B

Explanation:

The correct option ensures least-privilege by separating base EMR service access (no S3) from per-bucket access granted to team roles, and links those roles to AD groups via security configuration, allowing Kerberos/AD-based authorization without broad S3 access.
A) Incorrect: binds additional roles to EMR role policy instead of EMR EC2 service role trust, misconfiguring trust for instance profiles.
C) Incorrect: EMR service role grants full S3 access, violating least privilege.
D) Incorrect: trusts base EMR roles instead of configuring trust for the per-team bucket roles, leading to improper permission scoping.

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QUESTION: 22

A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala. Operational management should be limited. Which combination of components can meet these requirements? (Choose three.)

AWS Glue Data Catalog for metadata management
Amazon EMR with Apache Spark for ETL
AWS Glue for Scala-based ETL
Amazon EMR with Apache Hive for JDBC clients
Amazon Athena for querying data in Amazon S3 using JDBC drivers
Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore

Answer(s): A,C,E

Explanation:

A) AWS Glue Data Catalog for metadata management
C) AWS Glue for Scala-based ETL
E) Amazon Athena for querying data in Amazon S3 using JDBC drivers
A) Glue Data Catalog provides centralized metadata management with federation-compatible IAM and fine-grained access control, enabling cross-account and cross-service metadata access suitable for a data lake with tiered storage.
C) Glue supports PySpark and Scala-based ETL jobs via AWS Glue Studio/ETL, aligning with batch-based processing requirements.
E) Athena natively queries S3 data and supports JDBC-based connections through drivers, enabling legacy JDBC clients without heavy operational overhead.
B) EMR with Spark adds complexity and ongoing cluster ops; not minimal management.
D) EMR with Hive lacks federation metadata management and JDBC client support in a minimal setup.
F) EMR with Hive with RDS-backed metastore introduces unnecessary managed RDS dependency and operational overhead.

Reference:

https://d1.awsstatic.com/whitepapers/Storage/data-lake-on-aws.pdf

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QUESTION: 23

A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON les 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 inde nitely for compliance requirements.
Which solution meets the company's requirements?

Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Con gure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Con gure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Con gure a lifecycle policy to move the data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after object creation. Con gure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after object creation.
Use an AWS Glue ETL job to compress, partition, and convert the data into a columnar data format. Use Athena to query the processed dataset. Con gure a lifecycle policy to move the processed data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Con gure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long- term archival 7 days after the last date the object was accessed.
Use an AWS Glue ETL job to partition and convert the data into a row-based data format. Use Athena to query the processed dataset. Con gure a lifecycle policy to move the data into the Amazon S3 Standard-Infrequent Access (S3 Standard-IA) storage class 5 years after the object was last accessed. Con gure a second lifecycle policy to move the raw data into Amazon S3 Glacier for long-term archival 7 days after the last date the object was accessed.

Answer(s): A

Explanation:

A) Compress, partition, and convert to a columnar format; query via Athena; move processed data to S3 Standard-IA after 5 years; move raw data to Glacier after 7 days. This aligns with cost optimization for infrequently accessed data post-5 years, while preserving immutable raw data in Glacier for long-term archival per compliance; columnar format and partitioning optimize query performance and reduce scanned data, meeting sub-1-minute response. B) uses row-based format harming scan efficiency; C) and D) base lifecycle on last accessed date, which is unsuitable for immutable retention policy and may misprice data hot/cold, plus Glacier timing remains 7 days after last access, not alignment with 5-year access patterns.

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QUESTION: 24

An energy company collects voltage data in real time from sensors that are attached to buildings. The company wants to receive noti cations when a sequence of two voltage drops is detected within 10 minutes of a sudden voltage increase at the same building. All noti cations 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 noti cation system is subscribed to an Amazon Simple Noti cation 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. Use the 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 su cient Provisioned IOPS to meet current demand. Con gure the Lambda function 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 noti cations. 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 Con gure an AWS Lambda function to poll the message stream and publish to the SNS topic.

Answer(s): A

Explanation:

A) The combination of KSQL-like streaming with Spark on an auto-scaling EMR cluster provides real-time processing, scalable ingestion, and low-latency SNS notifications, meeting high availability and on-demand scaling as deployments expand to other cities.
B) REST+Lambda with RDS is not optimal for real-time sequence detection due to potential latency, limited scalability, and overhead of polling queries on relational DBs for streaming patterns.
C) Kinesis Data Firehose with Lambda provides near real-time but Firehose is primarily for delivery, not complex stateful sequence detection across events within a window.
D) Separate streams with Kinesis Data Analytics for Java adds unnecessary complexity; dual streams and polling increase latency and operational overhead without clear advantage over Spark on EMR.

Reference:

https://aws.amazon.com/kinesis/data-streams/faqs/

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Amazon DAS-C01: Skills Tested, Job Roles, and Study Tips

The AWS Certified Data Analytics - Specialty (DAS-C01) certification is designed for individuals who perform complex data analysis tasks and manage data analytics solutions on the Amazon Web Services platform. This certification is highly relevant for data engineers, data architects, and data analysts who are responsible for designing, building, and maintaining robust data pipelines and analytics architectures. Organizations across various industries, from finance to healthcare, rely on professionals with this Amazon certification to ensure their data infrastructure is scalable, secure, and cost-effective. By achieving this credential, candidates demonstrate a deep understanding of how to extract value from data using the comprehensive suite of AWS services, which is a critical skill set in modern enterprise environments. Employers value this certification because it provides objective proof that a candidate can navigate the complexities of cloud-based data ecosystems, from the initial ingestion of raw data to the final delivery of actionable business intelligence.

Professionals who pursue this certification typically have several years of experience working with data analytics technologies and a strong foundation in cloud computing principles. The exam validates the ability to define and architect AWS data analytics services that align with business requirements, ensuring that data is processed efficiently and securely. Because the role of a data analyst or engineer involves bridging the gap between raw data and strategic decision-making, this certification focuses heavily on the practical application of AWS services in real-world scenarios. Candidates who pass the exam are often tasked with optimizing data performance, managing data lifecycles, and implementing governance frameworks that protect sensitive information. Consequently, the certification serves as a professional benchmark that helps organizations identify talent capable of managing the entire lifecycle of data within the AWS cloud environment.

What the DAS-C01 Exam Covers

The DAS-C01 exam is structured to test a candidate's proficiency across several critical domains that define the data analytics lifecycle on AWS. Candidates must demonstrate a comprehensive understanding of data collection, which involves selecting the appropriate services to ingest data from various sources into the cloud, ensuring that the ingestion process is reliable and scalable. Once the data is collected, the exam tests knowledge of storage and data management, requiring candidates to understand the trade-offs between different storage solutions like data lakes and data warehouses, and how to organize data for optimal retrieval. Processing is another major pillar, where candidates are evaluated on their ability to transform and prepare data using services that handle batch and real-time processing tasks. Furthermore, the exam covers analysis and visualization, focusing on how to query data effectively and present insights through dashboards and reporting tools. Finally, security is woven throughout every aspect of the exam, as candidates must know how to implement encryption, manage access controls, and ensure compliance across all analytics services, which is why our practice questions emphasize these integrated workflows.

Among these domains, the processing and security aspects often present the most significant challenges for candidates because they require a deep, nuanced understanding of how different AWS services interact under load. For instance, designing a processing pipeline that handles high-velocity streaming data requires not just knowledge of individual services like Kinesis or EMR, but also an understanding of how to manage resource allocation, handle failures, and maintain data integrity throughout the transformation process. Similarly, security is not a standalone topic but a pervasive requirement that must be applied to every stage of the data pipeline, from securing data at rest in S3 buckets to managing fine-grained access control for users querying data via Athena or Redshift. Candidates must be prepared to answer complex, scenario-based questions that force them to choose the most secure and efficient architecture while balancing performance requirements and cost constraints. This level of technical depth is exactly what our practice questions aim to replicate, ensuring that you are prepared for the multi-faceted nature of the actual exam.

Are These Real DAS-C01 Exam Questions?

Our platform provides practice questions that are sourced and verified by the community, consisting of IT professionals and recent test-takers who have sat for the actual Amazon certification exam. Because these questions are contributed by individuals who have experienced the exam environment firsthand, our questions reflect what appears on the real exam. If you've been searching for DAS-C01 exam dumps or braindump files, our community-verified practice questions offer something more valuable, each question is verified and explained by IT professionals who recently passed the exam. We do not provide leaked or confidential content, as our goal is to help you understand the underlying concepts rather than memorize answers. This community-verified approach ensures that the material remains relevant and accurate, providing a reliable resource for your exam preparation.

The verification process is a collaborative effort where users actively participate in refining the content to ensure accuracy and clarity. When a question is posted, community members discuss the answer choices, debate the technical rationale behind the correct option, and flag any content that may be outdated or ambiguous based on their recent exam experience. This ongoing dialogue provides context that you simply cannot get from static study guides, as it highlights the specific nuances and "gotchas" that frequently appear on the actual test. By engaging with these discussions, you gain insight into the logic required to solve complex problems, which is far more effective for long-term retention than rote memorization. This collaborative environment is what makes our practice questions a trusted resource for candidates aiming to pass their certification exam.

How to Prepare for the DAS-C01 Exam

Effective exam preparation requires a structured approach that prioritizes hands-on experience and a deep understanding of AWS service architecture over simple memorization. We strongly recommend that you build a study schedule that allocates time for both theoretical review and practical experimentation in a sandbox AWS environment, where you can configure services like Kinesis, Glue, and Redshift to see how they behave in practice. Every practice question includes a free AI Tutor explanation that breaks down the reasoning behind the correct answer, so you understand the concept, not just the answer. This AI Tutor acts as a personal study assistant, helping you identify gaps in your knowledge and providing detailed explanations for why certain architectural choices are superior to others in specific scenarios. By combining official AWS documentation with consistent, hands-on practice, you will build the confidence needed to tackle the exam's challenging scenario-based questions.

A common mistake many candidates make is relying too heavily on memorizing facts or specific configurations, which often leads to failure when they encounter scenario-based questions that require applied knowledge. The DAS-C01 exam is designed to test your ability to make architectural decisions, meaning you must understand the "why" behind every service recommendation, not just the "what." To avoid this pitfall, focus on understanding the limitations and use cases of each service, and practice managing your time during your exam prep so that you do not get stuck on a single difficult question. Remember that the exam is a test of your professional judgment as much as your technical knowledge, so always evaluate the trade-offs between performance, cost, and security when analyzing a scenario. By consistently reviewing your incorrect answers and using the AI Tutor to clarify complex topics, you will develop the critical thinking skills necessary to succeed.

What to Expect on Exam Day

On the day of your Amazon certification exam, you should be prepared for a rigorous testing environment that focuses on your ability to apply knowledge to real-world data analytics scenarios. The exam typically consists of multiple-choice and multiple-response questions, which may include complex, multi-step scenarios that require you to select the best solution from several technically plausible options. You will have a set amount of time to complete the exam, and it is administered through a secure testing platform, either at a physical testing center or via an online proctored session. Because the questions are often lengthy and require careful reading, time management is a critical factor in your success, so you should practice answering questions under timed conditions during your exam preparation. Familiarizing yourself with the exam interface and the types of questions you will encounter helps reduce anxiety and allows you to focus entirely on the technical content during the actual test.

Who Should Use These DAS-C01 Practice Questions

These practice questions are intended for data professionals who are actively preparing for the AWS Certified Data Analytics - Specialty certification and have a solid foundation in data engineering or analytics. This exam is best suited for individuals with at least two years of hands-on experience working with AWS data services, as the questions assume a level of practical familiarity that cannot be gained through theory alone. Whether you are a data architect looking to validate your expertise or a developer transitioning into a specialized data role, these questions will help you gauge your readiness and identify areas where you need further study. By using these resources as part of your comprehensive exam preparation, you can ensure that you are fully equipped to handle the demands of the certification exam and advance your career in the cloud data space.

To get the most out of these practice questions, do not simply read the correct answer and move on; instead, engage deeply with the AI Tutor explanation and the community discussions provided for each item. If you find yourself consistently getting questions wrong in a specific topic area, take the time to revisit the official AWS documentation and perform additional hands-on labs to solidify your understanding. We recommend flagging questions that you find particularly challenging and revisiting them periodically to ensure that you have truly mastered the underlying concepts. By treating each practice question as a learning opportunity rather than a test of your current knowledge, you will build the confidence and competence required to pass the exam. Browse the questions above and use the community discussions and AI Tutor to build real exam confidence.

Amazon DAS-C01 Exam Prep AWS Certified Data Analytics - Specialty (DAS-C01) (Page 11 )

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QUESTION: 19

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Reference:

QUESTION: 20

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QUESTION: 21

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QUESTION: 22

Explanation:

Reference:

QUESTION: 23

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QUESTION: 24

Explanation:

Reference:

Amazon DAS-C01: Skills Tested, Job Roles, and Study Tips

What the DAS-C01 Exam Covers

Are These Real DAS-C01 Exam Questions?

How to Prepare for the DAS-C01 Exam

What to Expect on Exam Day

Who Should Use These DAS-C01 Practice Questions

Amazon DAS-C01 Exam Prep
AWS Certified Data Analytics - Specialty (DAS-C01) (Page 11 )