FIT5202 · Data Processing for Big Data
The Complete Exam Bible · S2 2026Data Processing for Big Data
FIT5202 Data Processing for Big Data is Monash University's postgraduate big-data unit in the Faculty of Information Technology, and it is organised around three themes: Volume (parallel databases and parallel algorithms), Complexity (machine learning at scale in Spark MLlib), and Velocity (real-time streaming with Kafka and Spark Structured Streaming). It is a method-and-cost-model unit — the marks live in naming the right parallel algorithm or ML method, applying the taught cost model or formula, keeping the direction words straight (speed-up versus scale-up, over- versus under-estimate), and interpreting the result. Monash runs FIT5202 assessment through Moodle as Assignment 1 (15%), a two-part Assignment 2 (Part A 15% + Part B 15%), weekly quizzes (5%) and marked lab attendance (5%), capped by a Final Examination worth 45% in the end-of-semester exam period. Two threshold hurdles apply — an in-semester hurdle and a final-exam hurdle — so steady work across the whole semester matters, and the FIT5202 result feeds the Weighted Average Mark (WAM) that later Monash data-science units build on.
What FIT5202 covers
FIT5202 is taught in three themes and this eleven-chapter map follows the teaching schedule through all of them. Chapters 1–4 cover the Volume theme — the big-data V's, parallel databases and partitioning, parallel join and parallel sort/group-by; Chapters 5–8 cover the Complexity/machine-learning theme — featurization, decision-tree classification, k-Means clustering and collaborative filtering in Spark MLlib; Chapters 9–11 cover the Velocity/streaming theme — Kafka and Spark Structured Streaming, stream joins and granularity reduction. Assessment runs through Moodle as Assignment 1, a two-part Assignment 2, weekly quizzes and marked labs, capped by a 45% final examination that is assessable across Weeks 1–12.
How FIT5202 is assessed
| Component | Weight | Format |
|---|---|---|
| Assignment 1 — Analysing Road Safety Data | 15% | Individual; parallel data processing in PySpark; opens ~Week 4, due ~Week 6 |
| Assignment 2 Part A — Building ML Models | 15% | Individual; Spark MLlib feature engineering + classification, due ~Week 11 |
| Assignment 2 Part B — Streaming Application | 15% | Individual; Kafka producer + Spark Streaming + consumer (ZIP of three notebooks), due ~Week 14 |
| Weekly Quizzes (Weeks 2-11) | 5% | Ten online quizzes (0.5 each); no GenAI |
| Lab Attendance (Sessions 2-11) | 5% | Marked lab participation; 80% attendance = full mark: min(5, raw x 1.25) |
| Final Examination | 45% | E-assessment, 2h10m, closed book, scientific calculator; Weeks 1-12; Q1 MCQ + Q2-6 written |
Entropy and information gain for a decision-tree split
- +2Dataset entropy H(S) = Σ pᵢ·log₁₀(1/pᵢ) with p(Yes) = 9/14 and p(No) = 5/14. H(S) = (9/14)·log₁₀(14/9) + (5/14)·log₁₀(14/5) = 0.643·0.1924 + 0.357·0.4472 = 0.1237 + 0.1597 = 0.283.
- +1Sunny branch (3 Yes, 2 No): H = (3/5)·log₁₀(5/3) + (2/5)·log₁₀(5/2) = 0.6·0.2218 + 0.4·0.3979 = 0.1331 + 0.1592 = 0.292.
- +1Overcast branch (4 Yes, 0 No) is pure, so H = 0. The Rainy branch (2 Yes, 3 No) is the mirror of Sunny, so H = 0.292.
- +1Weighted entropy after the split H(S, Outlook) = Σ (|Sᵢ|/|S|)·H(Sᵢ) = (5/14)·0.292 + (4/14)·0 + (5/14)·0.292 = (10/14)·0.292 = 0.209.
- +1Information gain IG(S, Outlook) = H(S) − H(S, Outlook) = 0.283 − 0.209 = 0.074. In ID3 you compute this for every attribute and split on the one with the largest gain.
Key terms
- Speed-up vs scale-up
- Speed-up = uniprocessor time / multiprocessor time (same task, less time); scale-up = small-system time / larger-system time on a proportionally larger task (bigger task, same time). Linear speed-up grows with the processor count; linear scale-up equals 1.
- Data partitioning
- Splitting a table across processors so scans run in parallel. Round-robin spreads records evenly (load-balanced, no semantic grouping); hash sends a record to hash(key) mod N (localises exact-match queries); range assigns by value ranges (localises range queries).
- Data skew (Zipf)
- Uneven distribution of records across processors, modelled by a Zipf degree θ (0 = no skew, 1 = highly skewed). Skew creates one overloaded processor that dominates elapsed time and undercuts the speed-up of joins and sorts.
- Cost model (network/shuffle)
- The optimizer's estimate C_total = C_CPU + C_I/O + C_network of an execution plan. In big data the network (shuffle) term usually dominates, so parallel joins and group-bys are judged mainly on how much data they move between processors.
- Information gain
- The drop in entropy after splitting on an attribute A: IG(S, A) = H(S) − Σ (|Sᵢ|/|S|)·H(Sᵢ). ID3 builds a decision tree by choosing, at each node, the attribute with the largest information gain.
- Cosine similarity
- The angle-based similarity of two rating vectors over co-rated items, sim(u, u') = Σ rᵤᵢ·rᵤ'ᵢ / (√Σ rᵤᵢ² · √Σ rᵤ'ᵢ²). It drives user-based collaborative filtering: the most similar users predict a target user's missing ratings.
FIT5202 FAQ
Is FIT5202 hard?
It is broad rather than deeply mathematical. The unit spans three themes — parallel databases and algorithms (speed-up, partitioning, joins, sorting), machine learning in Spark (entropy and information gain, k-Means, collaborative filtering) and real-time streaming (Kafka, windows, stream joins) — plus three programming assignments in PySpark. The maths is mostly logs, sums and cost models a scientific calculator can handle, so the challenge is keeping every algorithm, its cost model and its direction words straight across twelve weeks. Students who keep the weekly quizzes and labs warm on Moodle, rather than cramming through SWOTVAC, tend to find it manageable.
Can AI help me with FIT5202?
Yes, as a study aid rather than an answer machine. Sia is an AI tutor trained on how FIT5202 is actually taught and assessed: it can walk you through an entropy-and-information-gain split, explain why the network shuffle cost dominates a parallel join, trace a k-Means iteration or a symmetric-hash-join step by step, and check your reasoning on a practice problem. It does not complete graded assessment for you — the three assignments, quizzes and exam are your own work — and Monash University academic-integrity rules apply, including the no-GenAI rule in quizzes and the exam.
Where can I find past exam papers or practice for FIT5202?
Start on Moodle, where the unit posts the Week-12 recap, the eAssessment information and any released practice material, and rework the weekly quizzes and lab tasks — they are the closest match to the exam's style. This guide also includes a re-authored practice exam that mirrors the final's shape (a multiple-choice question plus written-response questions across parallel algorithms, Spark ML and streaming) with fresh numbers, and you can ask Sia to generate extra practice in the same style and then explain each step. Confirm exactly what is officially provided on your Semester-2 Moodle.
What are the FIT5202 hurdles and assessment rules?
Assessment is 55% in-semester (Assignment 1 15%, Assignment 2 Parts A + B 30%, weekly quizzes 5%, marked lab attendance 5%) and a 45% final examination. Two threshold hurdles apply: an in-semester hurdle (you must reach at least 45% of the in-semester marks) and a final-exam hurdle (you must score at least 45% on the exam itself). Monash's standard also expects an overall pass — confirm the exact thresholds, and the exact hurdle wording, on your Semester-2 Moodle and in the unit guide. Missing a hurdle can mean failing (NH) even with a strong mark on the other side, so cover every theme.
What is the FIT5202 exam like?
The final is an e-assessment on the Monash eAssessment platform, about 2 hours 10 minutes, closed book, with a scientific calculator permitted (a basic calculator may not be enough for the log and cost-model arithmetic). It is assessable across all material from Weeks 1–12 and has six questions — Q1 is multiple choice and Q2–Q6 are hybrid/written-response — with all questions to be answered. Budget roughly 20 minutes per question across the six, and confirm the exact duration, rules and permitted items on Moodle before the day.
How to study for the exam
Treat FIT5202 as three theme-toolkits rather than a reading unit. For Volume, drill the recurring calculations until they are automatic — speed-up versus scale-up, processor activation under round-robin/hash/range partitioning, external-sort passes, and the divide-and-broadcast cost model — and always state which cost term dominates. For Complexity, rehearse the entropy-and-information-gain split (log base 10, as taught), a k-Means iteration, precision/recall/F1 from a confusion matrix, and cosine-similarity prediction, each end to end with units. For Velocity, be able to trace a tumbling-versus-sliding window, a symmetric-hash-join arrival sequence, and a sensor-array reduce-then-merge. Keep the weekly Moodle quizzes and marked labs warm week by week rather than cramming in SWOTVAC, because both hurdles reward breadth: make sure you can start every theme's question before deepening the ones you find hardest. When a step will not click, ask Sia to re-explain that single line a different way and set you a fresh practice question in the same style — it teaches the method and checks your reasoning, and it never substitutes for your own graded work.
Your AI Information Technology tutor for FIT5202
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