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Streaming service management
Term Y12 Programming for the Web
Definition
Managing the delivery of live or on-demand media: buffering, adapting quality to bandwidth, servers and caching near users, and monitoring performance.
Example: Lower the video quality when bandwidth drops.
Found in: syllabus, 2025 HSC marking guidelines, sample exam and familiarisation guidelines, school notes.
Where it is used in the test
Syllabus area: Y12 Programming for the Web.
Question types it can appear in
- Short answer: Define, outline or explain it and tie it to the scenario.
- Extended response: Use it as a supporting point linked to the stimulus.
- Multiple choice: Pick the option that matches the definition or example.
Where it has appeared in the paper
Programming for the Web (Part A). Part letters follow each paper’s own layout.
Related past questions
- 2025 HSC Q18 (3 marks): Big data and web architecture Programming for the Web (Part A) · 3 marks
These are the questions the flashcard set links to this term. Open each one and compare your answer with its marking criteria.
Other questions that mention it
- Familiarisation Q14 Software Automation (Part C) · 8 marks
Study links
- Flashcard: Streaming service management in Programming for the Web (Year 12) · study that set
- Memorise: Effect of big data on web architecture: data mining, metadata, streaming
- Notes: Investigate the effect of big data on web architecture, including data mining, metadata,… (Programming for the Web (Year 12))
- Notes: Effect of big data on web architecture: data mining, metadata, streaming (Programming for the Web (Year 12))
- Master answer: hsc2025-q18 (3 marks, short answer (explain)) (Master model answers: Programming for the Web)
How to use it in an answer
A model sentence
“Managing the delivery of live or on-demand media: buffering, adapting quality to bandwidth, servers and caching near users, and monitoring performance.” Then apply it: Lower the video quality when bandwidth drops.
In a short answer, give the definition in your own words first, then tie it to the situation in the question. Do not copy the example above unless it fits the scenario.
Command words used with it
- Explain: Relate cause and effect; make the relationship between things evident; provide why and/or how. (2025 HSC Q18 (3 marks): Big data and web architecture)
Technique for the question types
- Short answer: One clear point per mark, use the stimulus and course terms, and answer the command word.
- Extended response: Plan with the command word, give a judgement or explanation supported by named details from the stimulus, use course terms and cover both sides when asked.
- Multiple choice: Read all four options, rule out wrong ones, pick one; no penalty for guessing so never leave blank.
What the marking guidelines and feedback say
Marker feedback: show how metadata and data mining RELATE; address them separately; focus on technical difficulties and PREVENTION.
Worked answers from the marking guidelines
2025 HSC Q18 (3 marks): Big data and web architecture (3 marks): criteria and sample answer
Criteria
3: Explains how metadata and data mining can be used to prevent technical difficulties during the broadcast. 2: Outlines how metadata AND/OR data mining can be used. 1: Identifies a feature of metadata or data mining.
Sample answer
Metadata describes the content, structure and meaning of raw data, enabling more effective, accurate and efficient data mining. Data mining identifies user preferences and viewing patterns from large datasets, helping streaming services personalise recommendations and identify times of predicted high demand. Streaming service management uses this along with real-time user data to balance server loads and deliver smooth playback experiences.
Common mistakes
- Use the term exactly as the syllabus words it, then link it to the scenario in the question; the 2025 feedback repeatedly rewards using the stimulus.
- Answer the command word: outline gives main points only, explain gives a reason or cause, discuss gives both sides.
Related terms
Same group in the syllabus: Big data · Data mining · Metadata