Number of questions: 63
Time allowed in minutes: 105
Required passing score: 71%
Languages: English, , Japanese
Section 1 – Fundamentals of Cognitive Computing
Define the main characteristics of a cognitive system
Explain neural nets
Explain machine learning technologies (supervised, unsupervised, reinforcement learning approaches)
Define a common set of use cases for cognitive systems
Define Precision, Recall, and Accuracy
Explain the importance of separating training, validation and test data
Measure accuracy of service
Define types of entities, relationships and co-references
Define Intents and Classes
Section 2 – Use cases of Cognitive Services
Select appropriate combination of cognitive technologies based on use-case and data format
Explain the uses of IBM Watson services available in the Application Starter Kits�
Describe the IBM Watson Conversation service
Explain use cases for integrating external systems such as Twitter, Weather API
Describe the IBM Watson Discovery service
Section 3 – Fundamentals of IBM Watson Developer Cloud
Distinguish cognitive services on IBM Watson Developer Cloud for which training is required or not
Provide examples of text classification using the IBM Watson Natural Language Classifier
Explain the IBM Watson SDKs available as part of the services on the IBM Watson Developer Cloud
Explain the IBM Watson REST APIs available as part of the services on the IBM Watson Developer Cloud
Explain and configure the IBM Watson Natural Language Classificer service
Explain and configure the IBM Watson Visual Recognition service
Explain and execute the IBM Watson Personality Insight service
Explain how the BM Watson Tone Analyzer service works
Explain, setup, configure and query the IBM Watson Discovery service
Explain and configure the IBM Watson Conversation service
Section 4 – Developing Cognitive applications using IBM Watson Developer Cloud
Call an IBM Watson service to analyze content
Describe the tasks required to implement the IBM Watson Conversational Agent / Digital Bot
Manipulate service outputs for consumption by other services
Define common design patterns for composing multiple IBM Watson services together
Explain the process to provision and use an instance of an IBM Watson Bluemix service instance
Explain the advantages of using IBM Bluemix as the cloud platform for Cognitive application development and deployment
Section 5 – Administration & DevOps for applications using IBM Watson services
Describe the process of obtaining credentials for IBM Watson services
Examine error logs provided by services
Job Role Description / Target Audience
This intermediate level technical professional is an individual who understands concepts essential to the development of applications using IBM Watson services. They have experience using the IBM Bluemix Platform-as-a-Service offering and are able to consume IBM Watson services in an application. This individual is able to perform these tasks with little to no assistance from product documentation, support or peers.
Key Areas of Competency:
Fundamentals of IBM Watson services
Use cases of Cognitive Services
Developing cognitive applications using IBM Watson services
Administering applications using IBM Watson services
Recommended Prerequisite Skills
Working knowledge of developing an application using IBM Watson services
Working knowledge of core Cloud services (monitoring, logging, scaling), and security
Working knowledge with designing, developing and deploying RESTful APIs
Working knowledge of use cases using IBM Watson services
Working knowledge of cognitive concepts such as intent, relationships, entities, and ground truth
Working knowledge of the application starter kits and demos available on the IBM Watson Developer Cloud
Working knowledge of open technologies like CloudFoundry and Git like repositories
Basic knowledge of machine learning methods and technologies
This certification requires 1 test(s).
Click on the link(s) below to see test details, test objectives, suggested training and sample tests.
Test C7020-230 – IBM Watson V3 Application Development
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Which type of learning do regression, support vector machines, and Bayesian classifiers typically fall under?
A. cognitive learning
B. supervised learning
C. unsupervised learning
D. reinforcement learning
What information does the IBM Watson Natural Language Understanding service extract from html, text, or web-based content when it analyzes entities?
A. text and title information
B. people, companies, organizations
C. topic keywords
D. subject-action-object relations
Why would the data crawler be used with the IBM Watson Discovery service?
A. The data crawler automates the upload of content to the Discovery service.
B. Use the data crawler when structured data must be mined from other services.
C. When working with the tooling, the data crawler can be used to upload sample documents.
D. When there is a business need to dynamically crawl websites, Twitter and constantly changing content.
In a neural network, what is the name of the region marked with a question mark?
A. Hidden Layer
B. Computational Layer
C. Cognitive Layer
D. Intelligence Layer
What is the formula for recall in a classification system?
A. True Positives/ (True Positives+False Negatives)
B. True Positives/ (True Positives+False Positives)
C. False Positives/ (True Negatives+False Negatives)
D. True Positives/ (True Positives+True Negatives)