PEARSON BTEC UNIT 5 DATA MODELLING
LEARNING AIM B AND C EXAM QUESTIONS
AND ANSWERS 2026 COMPLETE STUDY
GUIDE
◉ Text data. Answer: Rather than numbers, the data is text. It needs
to be converted into tabular data for processing. Techniques like
tokenization (e.g., Chat GPT-3 tokenizer, tiktoken, Byte pair
encoding) are used for this purpose. Example: Analyzing employee
emails for sentiment or topic classification.
◉ Image data. Answer: AI processes images as matrices of pixel
values. An image consists of a matrix of numbers between 0 and
255, indicating a pixel's intensity. Example: Identifying defective
products using machine vision on factory lines.
◉ Audio data. Answer: Audio data is often converted to arrays based
on waveform, quantifying the shape into numbers. Concepts related
to audio include pitch (measured in hertz), amplitude
(loudness/quietness), and timbre (uniqueness of sound). Example:
Transcribing customer service calls for analysis.
◉ Supervised machine learning. Answer: Involves training an
algorithm on a labeled dataset for predictions or decisions. Input
, data (only features) is paired with output data (labels or targets).
The algorithm learns patterns between the input and output.
Accuracy is a primary metric, measuring the proportion of correctly
classified instances. Example: Email spam detection where emails
are labeled as spam or not spam.
◉ Unsupervised machine learning. Answer: The algorithm is trained
on an unlabeled dataset. It identifies patterns or relationships
without guidance. The algorithm might cluster similar data points
based on shared features and can also reduce data dimensionality.
Evaluation is challenging due to the absence of a 'ground truth'.
Example: Customer segmentation for targeted marketing campaigns.
◉ Reinforcement learning. Answer: Has an agent interacting with an
environment to learn optimal decisions. The agent receives rewards
or penalties based on actions and updates its model to learn
beneficial actions. The goal is to maximize total reward. Total reward
during training is often used for evaluation, along with
benchmarking against a baseline. Example: Training a robot to
navigate a warehouse while avoiding obstacles.
◉ Generative AI. Answer: New examples of data that are useful.
◉ Large Language Models (LLM). Answer: Models that require large
amounts of data (billions) and parameters (billions).
LEARNING AIM B AND C EXAM QUESTIONS
AND ANSWERS 2026 COMPLETE STUDY
GUIDE
◉ Text data. Answer: Rather than numbers, the data is text. It needs
to be converted into tabular data for processing. Techniques like
tokenization (e.g., Chat GPT-3 tokenizer, tiktoken, Byte pair
encoding) are used for this purpose. Example: Analyzing employee
emails for sentiment or topic classification.
◉ Image data. Answer: AI processes images as matrices of pixel
values. An image consists of a matrix of numbers between 0 and
255, indicating a pixel's intensity. Example: Identifying defective
products using machine vision on factory lines.
◉ Audio data. Answer: Audio data is often converted to arrays based
on waveform, quantifying the shape into numbers. Concepts related
to audio include pitch (measured in hertz), amplitude
(loudness/quietness), and timbre (uniqueness of sound). Example:
Transcribing customer service calls for analysis.
◉ Supervised machine learning. Answer: Involves training an
algorithm on a labeled dataset for predictions or decisions. Input
, data (only features) is paired with output data (labels or targets).
The algorithm learns patterns between the input and output.
Accuracy is a primary metric, measuring the proportion of correctly
classified instances. Example: Email spam detection where emails
are labeled as spam or not spam.
◉ Unsupervised machine learning. Answer: The algorithm is trained
on an unlabeled dataset. It identifies patterns or relationships
without guidance. The algorithm might cluster similar data points
based on shared features and can also reduce data dimensionality.
Evaluation is challenging due to the absence of a 'ground truth'.
Example: Customer segmentation for targeted marketing campaigns.
◉ Reinforcement learning. Answer: Has an agent interacting with an
environment to learn optimal decisions. The agent receives rewards
or penalties based on actions and updates its model to learn
beneficial actions. The goal is to maximize total reward. Total reward
during training is often used for evaluation, along with
benchmarking against a baseline. Example: Training a robot to
navigate a warehouse while avoiding obstacles.
◉ Generative AI. Answer: New examples of data that are useful.
◉ Large Language Models (LLM). Answer: Models that require large
amounts of data (billions) and parameters (billions).
