Tag: Transformer Architecture

AI, AI-900, Artificial Intelligence (AI), Deep Learning, Microsoft Certification

Practice Exam Questions: Identify Features of the Transformer Architecture (AI-900 Exam Prep)

Practice Exam Questions

Question 1

What is the primary purpose of the self-attention mechanism in a Transformer model?

A. To reduce the size of the training dataset
B. To allow the model to focus on relevant parts of the input sequence
C. To replace the need for training data
D. To process words strictly in order

Correct Answer: B

Explanation:
Self-attention enables a Transformer to determine which words in a sentence are most relevant to one another, improving context understanding. It does not enforce strict order or reduce dataset size.

Question 2

Which feature allows Transformers to be trained more efficiently than recurrent neural networks (RNNs)?

A. Sequential word processing
B. Parallel processing of input data
C. Manual feature engineering
D. Rule-based language models

Correct Answer: B

Explanation:
Transformers process entire sequences in parallel, unlike RNNs that process tokens sequentially. This makes Transformers faster and more scalable.

Question 3

A key reason Transformers require positional encoding is because they:

A. Use convolutional layers
B. Process all input tokens at the same time
C. Rely on labeled data only
D. Perform unsupervised learning

Correct Answer: B

Explanation:
Because Transformers process words in parallel, positional encoding is needed to preserve information about word order in a sentence.

Question 4

Which type of AI workload most commonly uses Transformer-based models?

A. Time-series forecasting
B. Natural language processing
C. Image compression
D. Robotics control systems

Correct Answer: B

Explanation:
Transformers are primarily used for NLP tasks such as translation, summarization, and conversational AI.

Question 5

Which statement best describes the encoder–decoder architecture used in many Transformer models?

A. Both components generate output text
B. The encoder understands input, and the decoder generates output
C. The decoder trains the encoder
D. Both components store training data

Correct Answer: B

Explanation:
The encoder processes and understands the input sequence, while the decoder generates the output sequence based on that understanding.

Question 6

Why are Transformers better at handling long-range dependencies in text compared to earlier models?

A. They use fewer parameters
B. They rely on handcrafted grammar rules
C. They use attention to relate all words in a sequence
D. They process words one at a time

Correct Answer: C

Explanation:
Self-attention allows Transformers to evaluate relationships between all words in a sentence, regardless of distance.

Question 7

Which Azure scenario is most likely to involve a Transformer-based model?

A. Predicting tomorrow’s stock price
B. Detecting network hardware failures
C. Translating text between languages
D. Calculating average sales per region

Correct Answer: C

Explanation:
Language translation is a classic NLP task that relies heavily on Transformer architectures.

Question 8

What is a major advantage of Transformers over traditional sequence models?

A. They require no training data
B. They eliminate bias automatically
C. They improve scalability and performance
D. They work only with structured data

Correct Answer: C

Explanation:
Transformers scale efficiently due to parallel processing and attention mechanisms, improving performance on large datasets.

Question 9

Which statement about Transformers is TRUE?

A. They are rule-based AI systems
B. They process data strictly sequentially
C. They are a type of deep learning model
D. They are limited to image recognition

Correct Answer: C

Explanation:
Transformers are deep learning architectures commonly used for NLP tasks.

Question 10

Which feature enables a Transformer model to understand the context of a word based on surrounding words?

A. Positional encoding
B. Tokenization
C. Self-attention
D. Data labeling

Correct Answer: C

Explanation:
Self-attention allows the model to weigh the importance of surrounding words when interpreting meaning and context.

Quick Exam Tip

If you see keywords like:

  • attention
  • context
  • parallel processing
  • language understanding
  • Azure OpenAI

You’re almost certainly dealing with a Transformer-based model.

Go to the AI-900 Exam Prep Hub main page.

AI, AI-900, Artificial Intelligence (AI), Deep Learning, Microsoft Certification, Natural Language Processing (NLP)

Identify Features of the Transformer Architecture (AI-900 Exam Prep)

Where This Topic Fits in the Exam

  • Exam domain: Describe fundamental principles of machine learning on Azure (15–20%)
  • Sub-area: Identify common machine learning techniques
  • Focus: Understanding what Transformers are, why they matter, and what problems they solve — not how to code them

The AI-900 exam tests conceptual understanding, so you should recognize key features, benefits, and common use cases of the Transformer architecture.

What Is the Transformer Architecture?

The Transformer architecture is a type of deep learning model designed primarily for natural language processing (NLP) tasks.
It was introduced in the paper “Attention Is All You Need” and has since become the foundation for modern AI models such as:

  • Large Language Models (LLMs)
  • Chatbots
  • Translation systems
  • Text summarization tools

Unlike earlier sequence models, Transformers do not process data sequentially. Instead, they analyze entire sequences at once, which makes them faster and more scalable.

Key Features of the Transformer Architecture

1. Attention Mechanism (Self-Attention)

The core feature of a Transformer is self-attention.

Self-attention allows the model to:

  • Evaluate the importance of each word relative to every other word in a sentence
  • Understand context and relationships, even when words are far apart

Example:
In the sentence “The animal didn’t cross the road because it was tired”, self-attention helps the model understand what “it” refers to.

📌 Exam takeaway: Transformers use attention to understand context more effectively than older models.

2. Parallel Processing

Traditional models like RNNs process text one word at a time.
Transformers process all words in parallel.

Benefits:

  • Faster training
  • Better performance on large datasets
  • Improved scalability in cloud environments (like Azure)

📌 Exam takeaway: Transformers are efficient and scalable because they don’t rely on sequential processing.

3. Encoder–Decoder Structure

Many Transformer-based models use an encoder–decoder architecture:

  • Encoder:
    • Reads and understands the input (e.g., a sentence in English)
  • Decoder:
    • Generates the output (e.g., the translated sentence in Spanish)

📌 Exam takeaway: Transformers often use encoders to understand input and decoders to generate output.

4. Positional Encoding

Because Transformers process words in parallel, they need a way to understand word order.

Positional encoding:

  • Adds information about the position of each word
  • Allows the model to understand sentence structure and sequence

📌 Exam takeaway: Transformers use positional encoding to retain word order information.

5. Strong Performance on Natural Language Tasks

Transformers are especially effective for:

  • Text translation
  • Text summarization
  • Question answering
  • Chatbots and conversational AI
  • Sentiment analysis

📌 Exam takeaway: Transformers are closely associated with natural language processing workloads.

Why Transformers Are Important in Azure AI

Microsoft Azure AI services rely heavily on Transformer-based models, especially in:

  • Azure OpenAI Service
  • Azure AI Language
  • Conversational AI and copilots
  • Search and knowledge mining

Understanding Transformers helps explain why modern AI solutions are more accurate, context-aware, and scalable.

Transformers vs Earlier Models (High-Level)

FeatureEarlier Models (RNNs/CNNs)Transformers
Sequence processingSequentialParallel
Context handlingLimitedStrong
Long-range dependenciesDifficultEffective
Training speedSlowerFaster
NLP performanceModerateState-of-the-art

📌 Exam focus: You don’t need technical depth — just understand why Transformers are better for language tasks.

Common Exam Pitfalls to Avoid

  • ❌ Thinking Transformers replace all ML models
  • ❌ Assuming Transformers are only for images
  • ❌ Confusing Transformers with traditional rule-based NLP

✅ Remember: Transformers are deep learning models optimized for language and sequence understanding.

Key Exam Summary (Must-Know Points)

If you remember nothing else, remember this:

  • Transformers are deep learning models
  • They rely on self-attention
  • They process data in parallel
  • They are especially effective for natural language processing
  • They power modern AI services in Azure

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