S2000-027
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S2000 027 AI Fundamentals
The IBM Certified Specialty - AI Fundamentals v1 (S2000-027) teaches core AI, ML, and DL concepts, NLP, computer vision, generative AI, and IBM Watson services, enabling learners to apply and analyze AI solutions across data lifecycles.
90
Minutes
60
Questions
60/100
Passing Score
$200
Exam Cost
Who Should Take This
Data scientists, AI engineers, and solution architects with foundational knowledge of programming and statistics benefit from this certification. It targets professionals seeking to validate their ability to design, implement, and evaluate AI models using IBM’s ecosystem, and to advance toward senior AI roles.
What's Covered
1
Domain 1: AI/ML/DL Fundamentals and Core Concepts
2
Domain 2: Natural Language Processing and Computer Vision
3
Domain 3: Generative AI and Foundation Models
4
Domain 4: IBM AI Portfolio and Watson Services
5
Domain 5: Data Requirements and AI Model Lifecycle
6
Domain 6: AI Ethics, Responsible AI, and Governance
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
75 learning goals
1
Domain 1: AI/ML/DL Fundamentals and Core Concepts
3 topics
Artificial Intelligence Foundations
- Define artificial intelligence and distinguish it from traditional programming approaches including rule-based systems
- Identify the three main types of AI (narrow, general, super) and classify current AI applications into appropriate categories
- Compare supervised, unsupervised, and reinforcement learning paradigms with specific examples of each approach
- Analyze the relationship between artificial intelligence, machine learning, and deep learning as nested disciplines
- Evaluate the strengths and limitations of symbolic AI versus connectionist approaches in solving different problem types
Machine Learning Core Principles
- Describe the machine learning workflow including data collection, preprocessing, training, validation, and deployment phases
- Apply cross-validation techniques and explain overfitting, underfitting, and the bias-variance tradeoff in model performance
- Select appropriate evaluation metrics (accuracy, precision, recall, F1-score, AUC-ROC) for different ML problem types
- Analyze feature selection and engineering techniques including dimensionality reduction methods like PCA
- Compare ensemble methods (bagging, boosting, stacking) and assess their impact on model performance and interpretability
Deep Learning and Neural Networks
- Explain the structure and function of artificial neurons, activation functions, and basic neural network architectures
- Describe backpropagation algorithm and gradient descent optimization for training neural networks
- Apply different neural network architectures (CNNs, RNNs, LSTMs, Transformers) to appropriate problem domains
- Analyze hyperparameter tuning strategies including learning rate scheduling, batch size selection, and regularization techniques
- Evaluate transfer learning approaches and pre-trained models for reducing training time and improving performance
2
Domain 2: Natural Language Processing and Computer Vision
2 topics
Natural Language Processing Fundamentals
- Define key NLP concepts including tokenization, stemming, lemmatization, and part-of-speech tagging techniques
- Explain text preprocessing steps and feature extraction methods including TF-IDF, n-grams, and word embeddings
- Apply sentiment analysis, named entity recognition, and text classification techniques to real-world text data
- Implement basic chatbot functionality using rule-based approaches and simple machine learning models
- Analyze the evolution from traditional NLP to transformer-based models including BERT, GPT, and T5 architectures
Computer Vision Applications
- Describe fundamental computer vision tasks including image classification, object detection, and image segmentation
- Explain image preprocessing techniques including normalization, augmentation, and feature extraction methods
- Apply convolutional neural networks (CNNs) for image recognition and classification tasks
- Implement object detection algorithms including YOLO, R-CNN variations, and evaluate their performance metrics
- Analyze facial recognition systems, optical character recognition (OCR), and medical image analysis applications
3
Domain 3: Generative AI and Foundation Models
3 topics
Generative AI Concepts
- Define generative AI and distinguish it from discriminative AI models in terms of objectives and outputs
- Explain the architecture and training process of Generative Adversarial Networks (GANs) including generator and discriminator components
- Apply Variational Autoencoders (VAEs) for generating new data samples and understanding latent space representations
- Compare different generative model architectures including GANs, VAEs, autoregressive models, and diffusion models
- Analyze the quality assessment methods for generated content including FID scores, BLEU scores, and human evaluation metrics
Foundation Models and Large Language Models
- Describe foundation models and their characteristics including pre-training on large datasets and few-shot learning capabilities
- Explain the transformer architecture components including self-attention mechanisms, positional encoding, and multi-head attention
- Apply fine-tuning techniques for adapting pre-trained language models to specific tasks and domains
- Implement parameter-efficient fine-tuning methods including LoRA, adapters, and prompt tuning approaches
- Evaluate the scaling laws and emergent capabilities of large language models across different parameter sizes
Prompt Engineering Basics
- Define prompt engineering and explain its importance in optimizing interactions with large language models
- Identify different prompting techniques including zero-shot, few-shot, and chain-of-thought prompting methods
- Apply prompt design principles including clarity, specificity, context provision, and output format specification
- Create effective prompts for various tasks including text generation, summarization, translation, and question answering
- Analyze prompt effectiveness and iterate on prompt design to improve model outputs and reduce hallucinations
4
Domain 4: IBM AI Portfolio and Watson Services
3 topics
IBM watsonx Platform
- Describe the three pillars of IBM watsonx including watsonx.ai, watsonx.data, and watsonx.governance components
- Explain watsonx.ai studio capabilities for foundation model development, tuning, and deployment workflows
- Apply watsonx foundation models for various use cases including code generation, content creation, and business automation
- Configure watsonx.data for managing structured and unstructured data across hybrid cloud environments
- Evaluate watsonx.governance features for AI model lifecycle management, risk assessment, and compliance monitoring
Watson AI Services
- Identify Watson AI services including Watson Assistant, Watson Discovery, Watson Natural Language Understanding, and Watson Speech services
- Explain Watson Assistant architecture including intents, entities, dialog flows, and integration capabilities
- Apply Watson Discovery for document analysis, knowledge extraction, and intelligent search implementations
- Implement Watson Natural Language Understanding for sentiment analysis, emotion detection, and entity extraction tasks
- Compare Watson AI services capabilities with third-party alternatives and assess integration complexity and costs
Integration and Deployment
- Describe IBM Cloud Pak for Data and its role in AI model development and deployment pipelines
- Explain API integration patterns for consuming Watson services in web applications and mobile apps
- Apply IBM Watson Machine Learning service for model deployment, scoring, and automated retraining processes
- Configure monitoring and logging for deployed AI models using IBM Cloud and Watson OpenScale capabilities
- Analyze cost optimization strategies for IBM AI services including usage patterns, pricing tiers, and resource scaling
5
Domain 5: Data Requirements and AI Model Lifecycle
2 topics
Data Requirements for AI
- Define data quality dimensions including accuracy, completeness, consistency, timeliness, and relevance for AI applications
- Explain data collection strategies and sampling techniques for creating representative training datasets
- Apply data preprocessing techniques including cleaning, normalization, feature scaling, and handling missing values
- Implement data augmentation methods for increasing dataset size and improving model generalization
- Evaluate data bias detection and mitigation strategies throughout the AI development lifecycle
Training and Inference Processes
- Describe the distinction between training, validation, and testing datasets and their roles in model development
- Explain computational requirements and infrastructure considerations for training large AI models
- Apply distributed training techniques and optimize training performance using GPUs and specialized hardware
- Implement model versioning and experiment tracking systems for managing multiple training iterations
- Analyze inference optimization techniques including model compression, quantization, and edge deployment strategies
6
Domain 6: AI Ethics, Responsible AI, and Governance
2 topics
AI Ethics and Responsible AI
- Define key AI ethics principles including fairness, accountability, transparency, explainability, and privacy protection
- Explain algorithmic bias types including historical, representation, measurement, and evaluation bias in AI systems
- Apply fairness metrics and bias detection tools to evaluate AI model performance across different demographic groups
- Implement explainable AI techniques including LIME, SHAP, and attention visualization for model interpretability
- Evaluate privacy-preserving techniques including differential privacy, federated learning, and data anonymization methods
AI Governance and Use Cases
- Describe AI governance frameworks and regulatory considerations including GDPR, CCPA, and emerging AI legislation
- Explain risk management strategies for AI deployment including model monitoring, drift detection, and incident response
- Apply AI impact assessment methodologies for evaluating societal, economic, and environmental effects of AI systems
- Analyze AI use cases across industries including healthcare, finance, retail, manufacturing, and transportation sectors
- Evaluate the balance between AI innovation and responsible deployment considering stakeholder interests and societal impact
Scope
Included Topics
- All domains of S2000-027 IBM Certified Specialty - AI Fundamentals v1: AI fundamentals: AI/ML/DL concepts, neural networks, NLP, computer vision; generative AI, foundation models, prompt engineering basics; IBM AI portfolio (watsonx, Watson services); data requirements f.
- Exam-specific technical content covering or AI, training/inference; AI ethics, responsible AI, governance; AI use cases across industries..
Not Covered
- Topics outside the S2000-027 exam scope and other certification levels.
- Current pricing, promotional offers, and vendor-specific values that change over time.
- Implementation details for competing vendor products and platforms.
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