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IBM Watson Application Developer V3.1 certification exam provides an opportunity for individuals to showcase their knowledge and skills in developing enterprise-scale applications. C1000-012J exam tests the candidate's ability to build applications using Watson services such as Watson Assistant, Watson Discovery, Watson Language Translator, Watson Natural Language Understanding, and Watson Speech to Text. C1000-012J exam also covers topics such as application design, deployment, and management.
Reference: https://www.ibm.com/certify/exam?id=C1000-012
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The IBM C1000-012J exam consists of 60 multiple-choice questions, and candidates are given 90 minutes to complete the exam. The questions are designed to test the candidate's ability to understand and apply IBM Watson technologies in various situations. Candidates will be tested on their knowledge of Watson services, such as Watson Assistant, Watson Discovery, and Watson Language Translator, and their ability to integrate these services into their applications.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of IBM Watson AI Services | 25% | - Watson SDKs and REST APIs - Tone Analyzer, Personality Insights, Visual Recognition - Natural Language Classifier - Natural Language Understanding - Watson Discovery - Watson Assistant (Conversational Agent) |
| Topic 2: Developing AI Applications on IBM Cloud | 25% | - Provision Watson service instances - Design patterns for combining Watson services - Cloud Foundry, Git, IBM Cloud basics - RESTful API development - Use Node.js, JavaScript, HTML/CSS |
| Topic 3: Administration & DevOps | 15% | - Application security - Monitoring, logging, scaling - Domain adaptation with Watson Knowledge Studio |
| Topic 4: Use Cases for AI & Watson Services | 15% | - Select AI technologies for specific use cases - Integrate external systems and APIs - Watson starter kits and demos |
| Topic 5: Fundamentals of Artificial Intelligence | 20% | - AI concepts: intent, entities, relationships, ground truth - Data splitting: training, validation, test sets - Machine learning types: supervised, unsupervised, reinforcement - Accuracy, precision, recall metrics |
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