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| Section | Objectives |
|---|---|
| Responsible AI & Ethics | - AI governance and compliance (e.g., GDPR) - Bias detection and mitigation |
| Model Development & Machine Learning | - Supervised and unsupervised learning - Model training and optimization |
| Deployment & AI Operations | - Model deployment pipelines - Model monitoring and drift management |
| AI Fundamentals & Business Applications | - AI fundamentals and architectures - AI applications in business operations |
| AI Strategy & Business Transformation | - AI-driven business use cases - ROI and value creation with AI |
| Data Preparation & Feature Engineering | - Data cleaning and preprocessing - Feature engineering techniques |
| Model Evaluation & Metrics | - Performance metrics (precision, recall, F1, AUC-ROC) - Model validation and selection |
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14. Frage
Which one of the following is a CORRECT benefit for using AI in product development?
Antwort: A
Begründung:
The correct answer is D. a and b only because AI provides strong benefits across the product development life cycle, especially by improving speed, decision quality, and data-driven design. Statement A is correct because AI can shorten the product development life cycle by automating research, analyzing customer feedback, generating product ideas, supporting rapid prototyping, improving testing, and helping teams identify risks or opportunities earlier.
Statement B is also correct because applying AI throughout the PDLC helps organizations use data consistently at every stage, from ideation and market research to design, testing, launch, and post-launch improvement. This means products are not only based on data at the beginning but continue to reflect data- driven insights throughout development.
Statement C is not the best answer because "increase the product feature" is unclear and grammatically incomplete. AI may help improve features or identify new feature opportunities, but the statement is not as accurate as A and B. Therefore, the best answer is D. a and b only .
15. Frage
Which of the following is a CORRECT NLP task?
Antwort: D
Begründung:
The correct answer is E. a, b and c only because tokenization, Part-of-Speech tagging, and Question Answering are all valid natural language processing tasks. NLP focuses on enabling machines to process, analyze, understand, and generate human language for business and technical applications.
Tokenization is a basic NLP task where text is divided into smaller units such as words, subwords, or tokens.
This step helps models process language in a structured way. Part-of-Speech tagging is also an NLP task because it identifies the grammatical role of words, such as nouns, verbs, adjectives, and adverbs. This helps systems understand sentence structure and meaning. Question Answering is another important NLP task where a system interprets a user's question and generates or retrieves the most relevant answer from data, documents, or knowledge sources.
Since all three options represent correct NLP tasks, the best answer is E. a, b and c only .
16. Frage
What is solution architecture?
Antwort: B
Begründung:
Solution architecture is the structured design blueprint that explains how a business or technology solution will be built, integrated, operated, secured, and scaled. Option A is correct because solution architecture guides development and implementation by defining components, workflows, integrations, platforms, data flows, and technical decisions. Option B is also correct because a complete solution architecture considers the whole system, including infrastructure, networking, security, compliance, operations, cost, performance, and reliability. These elements are necessary to ensure that the solution can work in a real enterprise environment.
Option C is also correct because solution architecture does not only address current business requirements. It also supports future growth by planning for scalability, maintainability, adaptability, and long-term business success. Since all three statements accurately describe solution architecture, the most complete and correct answer is E. a, b and c only .
17. Frage
Choose the CORRECT statement for SOFT Launch.
Antwort: C
Begründung:
The correct answer is E. a, b and c only because all three statements correctly describe a soft launch. A soft launch is a limited and controlled release of a new product, service, platform, or feature before a full public launch. Organizations use it to test the product in a real or semi-real market environment while reducing risk and maintaining control over the launch process.
Statement A is correct because a soft launch is not a large-scale public release; it is usually restricted to a smaller audience, region, user group, or market segment. Statement B is also correct because companies may handpick early users, beta testers, internal customers, loyal customers, or selected market participants to try the product first. Statement C is correct because a soft launch helps the company observe customer reactions, collect feedback, identify defects, test adoption, validate messaging, and estimate customer acquisition costs.
Since A, B, and C are all accurate, the best answer is E. a, b and c only .
18. Frage
Select the BEST choice for ML solutions architecture coverage.
Antwort: C
Begründung:
The correct answer is E. a, b and c only because ML solution architecture must cover the complete path from business need to technical implementation. Business understanding is essential because an ML solution should begin with a clear problem statement, business objective, success criteria, expected value, and operational impact. Without business understanding, the model may solve the wrong problem or fail to create measurable value.
Identification and verification of ML techniques are also part of ML solution architecture because teams must choose suitable algorithms, validate model approaches, compare methods, and confirm that the selected technique fits the data, use case, performance expectations, and business constraints. System architecture of the ML technology platform is equally important because ML solutions require data pipelines, infrastructure, compute resources, model deployment environments, monitoring, security, scalability, and integration with enterprise systems.
Since all three areas are important parts of ML solution architecture coverage, the best answer is E .
19. Frage
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