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| Section | Weight | Objectives |
|---|---|---|
| AI in Business & Consulting | 25% | - Implementation Planning & Change Management - AI Strategy & Roadmap Development - Identifying AI Use Cases & Opportunities - Cost-Benefit Analysis & ROI Calculation - AI Project Management & Governance |
| AI Fundamentals & Concepts | 15% | - AI Lifecycle and Workflow - Introduction to Artificial Intelligence
|
| AI Technologies & Tools | 15% | - Frameworks: TensorFlow, PyTorch, Scikit-learn - Cloud AI Platforms (AWS, Azure, Google Cloud) - AI Applications: NLP, Computer Vision, Predictive Analytics - Automation & RPA Integration |
| Ethics, Governance & Future Trends | 10% | - Data Privacy, Security & Compliance - Emerging Trends & Future of AI - AI Ethics, Bias & Fairness - Explainable AI & Transparency |
| Data Preparation & Engineering | 15% | - Data Cleaning, Preprocessing & Transformation - Data Collection & Sources - Data Quality & Governance - Feature Engineering & Selection |
| Machine Learning & Algorithms | 20% | - Model Training, Evaluation & Optimization - Popular Algorithms
|
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40. Frage
Which of the following is a CORRECT NLP task?
Antwort: C
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 .
41. Frage
What is solution architecture?
Antwort: E
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 .
42. Frage
Which of the following is NOT a learning category for the ML model?
Antwort: B
Begründung:
The correct answer is D. Semi Reinforcement learning because it is not commonly recognized as a standard learning category for machine learning models. The major machine learning categories include supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Supervised learning uses labeled datasets where the model learns from known input-output examples. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Reinforcement learning trains an agent through interaction with an environment using rewards and penalties. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data to improve learning when fully labeled datasets are limited.
"Semi Reinforcement learning" is not normally listed as a core ML learning category in standard AI and machine learning learning paths. Therefore, among the given options, the one that is NOT a learning category for the ML model is D. Semi Reinforcement learning .
43. Frage
Select the MOST CORRECT statement for Few-shot learning.
Antwort: E
Begründung:
The correct answer is E. b and c only because few-shot learning means a model learns or adapts to a new task using only a small number of examples. In generative AI and large language model usage, few-shot prompting often provides a few demonstrations so the model can understand the expected pattern, format, classification logic, or response style. Option B is correct because few-shot learning uses a limited number of examples rather than a large training dataset.
Option C is also correct because few-shot learning depends on the model's prior knowledge learned during pretraining. The model uses that existing knowledge to generalize from the small set of examples and apply the same logic to new inputs. Option A is not the best statement because "a large number of examples" does not match the idea of few-shot learning. Therefore, the most correct answer is E. b and c only .
44. Frage
What is the main advantage of using deep learning over traditional machine learning?
Antwort: A
Begründung:
The correct answer is B. Better performance with large datasets . Deep learning is especially effective when large volumes of data are available because deep neural networks can automatically learn complex patterns, representations, and relationships from data. Unlike many traditional machine learning methods that often depend heavily on manual feature engineering, deep learning models can learn hierarchical features directly from raw or semi-processed data.
Option A is incorrect because deep learning usually requires more data, not less, to perform well. Option C is also incorrect because deep learning typically requires greater computational power, especially for training large models with many layers and parameters. Option D is incorrect because deep learning is not limited to structured data. It is widely used with unstructured data such as images, audio, video, and natural language.
Therefore, the main advantage of deep learning over traditional machine learning is B. Better performance with large datasets .
45. Frage
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