Where This Fits in the Exam
- Exam Domain: Describe fundamental principles of machine learning on Azure (15–20%)
- Sub-Domain: Identify common machine learning techniques
- Topic: Identify features of deep learning techniques
On the AI-900 exam, deep learning questions focus on what makes deep learning distinct, when it is used, and what types of problems it solves well.
What Is Deep Learning?
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to learn complex patterns in data.
- Inspired by how the human brain works
- Uses many layers to extract increasingly abstract features
- Particularly effective with large and complex datasets
Key exam idea:
Deep learning uses multi-layer neural networks to automatically learn features from data.
Key Features of Deep Learning Techniques
Multi-Layer Neural Networks
Deep learning models consist of:
- An input layer
- One or more hidden layers
- An output layer
Each layer learns progressively more complex representations of the data.
This “depth” is what differentiates deep learning from traditional machine learning models.
Automatic Feature Extraction
Traditional machine learning often requires manual feature engineering.
Deep learning:
- Automatically learns relevant features
- Reduces the need for human-designed features
- Is well-suited for unstructured data
This is a high-frequency exam point.
Works Well with Unstructured Data
Deep learning excels at handling:
- Images
- Audio
- Video
- Natural language text
These data types are difficult for traditional ML models but ideal for deep neural networks.
Requires Large Amounts of Data
Deep learning models typically:
- Perform better with large datasets
- Require significant training data
- Benefit from increased data volume and variety
On the exam, deep learning is often associated with big data scenarios.
High Computational Requirements
Deep learning models:
- Require more processing power
- Often use GPUs for training
- Take longer to train than simpler models
You don’t need hardware details for AI-900 — just recognize that deep learning is computationally intensive.
Common Deep Learning Use Cases
Computer Vision
- Image classification
- Facial recognition
- Object detection
Natural Language Processing
- Language translation
- Sentiment analysis
- Text generation
Speech Recognition
- Voice assistants
- Speech-to-text systems
These scenarios frequently appear in AI-900 questions tied to deep learning.
Deep Learning vs Traditional Machine Learning
This comparison is commonly tested.
| Aspect | Traditional ML | Deep Learning |
|---|---|---|
| Feature engineering | Manual | Automatic |
| Model complexity | Simpler models | Multi-layer neural networks |
| Data requirements | Smaller datasets | Large datasets |
| Best for | Structured data | Unstructured data |
| Compute needs | Lower | Higher |
Azure Context for AI-900
In Azure, deep learning is commonly associated with:
- Azure Machine Learning
- AI services built on deep neural networks
- Vision, speech, and language workloads
You are not expected to:
- Build neural networks
- Choose architectures
- Write training code
Focus on identifying features and use cases.
Common Exam Traps and Misconceptions
- ❌ Deep learning is required for all ML problems
- ❌ Deep learning works best with small datasets
- ❌ Deep learning requires manual feature selection
- ✅ Deep learning excels at complex, unstructured data tasks
Key Takeaways for the Exam
- Deep learning uses multi-layer neural networks
- It automatically learns features from data
- It works best with large datasets
- It is ideal for images, text, audio, and video
- It requires more computational resources than traditional ML
Go to the Practice Exam Questions for this topic.
Go to the AI-900 Exam Prep Hub main page.
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