Question 66 of 985
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is artificial intelligence (AI) in the context of computer science?
⚠ Common exam trap
A common mix-up: candidates confuse AI with the hardware or tools used to implement it, such as mistaking a GPU for AI itself, or thinking AI is synonymous with a specific programming language like Python.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Software that enables machines to simulate human intelligence and learn from data
Artificial intelligence (AI) in computer science refers to software systems that can perform tasks typically requiring human intelligence, such as learning from data, reasoning, and decision-making. This definition encompasses machine learning, deep learning, and other subfields where models are trained on data to improve performance over time, rather than following explicitly programmed rules.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A type of computer hardware that processes data faster than traditional CPUs
Why it's wrong here
This option conflates the software discipline of AI with the physical compute substrate on which AI workloads run. While GPUs, TPUs, and other accelerators can execute certain matrix operations faster than a traditional CPU, AI itself is not the hardware — it is the set of algorithms and learned models producing intelligent behavior. Without AI software, faster hardware merely performs arithmetic.
- ✓
Software that enables machines to simulate human intelligence and learn from data
Why this is correct
This is the intended definition: artificial intelligence refers to software engineering techniques that build systems capable of perceiving their environment, reasoning, making decisions, and improving from experience or data. These abilities come from models such as neural networks and from machine-learning processes that adjust parameters based on examples, allowing the system to perform tasks that typically require human intelligence.
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A programming language used to write algorithms
Why it's wrong here
This option mistakes a vehicle for the destination: AI systems are developed using programming languages such as Python or C++, but no language itself is AI. A language provides syntax and libraries for coding algorithms; the intelligence emerges from the algorithmic framework, training data, and optimization objectives, not from the language's keywords or compiler. Many languages can implement AI, which shows AI is language-agnostic.
- ✗
A type of database that stores structured information
Why it's wrong here
This option focuses on data persistence and retrieval, which are supporting services rather than defining capabilities of AI. A database stores, indexes, and queries structured records, whereas an AI system interprets that data, discovers patterns, and predicts or acts. Data is raw material for AI; often AI also uses unstructured data like images and text that do not fit conveniently into structured tables.
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Last reviewed: Jun 11, 2026
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