WBCHSE HS Artificial Intelligence & Data Science exam-day guide
WBCHSE HS Artificial Intelligence & Data Science · Prepare for the day ahead.
Your checklist
Admit card / registration slip
Carry the original admit card issued for the HS examination. Keep a photocopy in a separate pocket. Some centres also ask for the school registration certificate - [needs_review] confirm with your school which exact document the external examiner will check on the day.
School identity card
A valid school ID with your photograph and roll number. If your ID has expired or has an old photograph, get it reissued before the practical window opens rather than arguing at the gate.
Practical file / lab notebook / portfolio
All experiments, programs, and observation records written up, indexed, and dated. Get every page signed and graded by your subject teacher beforehand - an unsigned file is the single most common reason candidates lose easy marks. [needs_review] confirm with your teacher whether the file must be submitted before the exam or carried to the viva.
Printed project report and source-code listing
If your syllabus includes a project or case study, carry a clean printed copy: problem statement, dataset description, method, code listing, results, and conclusion. Keep the code listing numbered so you can point to a line during the viva.
Pen, pencil, eraser, sharpener, scale
Two blue or black ball pens plus one pencil for any diagram, flowchart, or table work. Do not rely on borrowing from a neighbour; examiners often ask candidates to write a short procedure or draw a flow diagram on the spot.
Scientific calculator
[needs_review] Confirm with your school whether a non-programmable calculator is allowed in the AI lab; some centres permit it for statistics and error calculations, others supply nothing and expect mental or rough-sheet arithmetic.
USB drive with your project code and dataset
[needs_review] Many centres do not allow any external storage device into the lab and expect you to work on the school's own machine. Ask your teacher in advance; if pendrives are banned, print the code instead and keep the drive at home.
Know your environment before the day
Be sure which tools your centre actually has: Python 3 with IDLE or VS Code, Jupyter Notebook, and libraries such as numpy, pandas, matplotlib, and scikit-learn. If a library you practised on is missing, plan the plain-Python fallback now, not in the exam hall.
Water bottle and a light snack
Practical sessions can run long and reporting times are early. A transparent water bottle is normally fine; keep the snack in your bag outside the lab and eat after your turn, not at the workstation.
Written revision sheet kept outside the lab
One page of syntax reminders - reading a CSV with pandas, train-test split, fitting a simple model, plotting a line chart, writing a SQL SELECT with GROUP BY. Leave it in your bag; it is for the minutes before entry, never at the desk.
Confirmed reporting time and venue
Note the exact reporting time, lab room, and whether the practical is held at your own school or a different centre with an external examiner. [needs_review] Confirm both from the school notice board or your subject teacher, since the board's general schedule does not fix individual centre timings.
Roll number written correctly
Practise writing your roll number, paper code, and subject name exactly as printed on the admit card. A wrong roll number on the answer script or program file can delay or invalidate evaluation.
Plan the day
Do a full dress rehearsal on the centre's actual setup if you can get lab access: open the IDE, load a CSV, clean the data, train one simple model, plot one chart, and save the output. Time yourself. Write down every error you hit and fix it while you still have a teacher to ask.
Finish and bind the practical file. Check that the index matches the page numbers, every experiment has aim, procedure, code, output, and conclusion, and nothing is left blank for a friend to fill in later. Get the teacher's signature on any newly added pages.
Prepare two-minute spoken answers for the questions most likely to be asked: what your project does, why you chose that dataset, what a training and test split is for, what overfitting means, the difference between a list and a DataFrame, and what supervised versus unsupervised learning means. Say them aloud, not just in your head.
Reread your own file and project rather than new material. Rehearse the exact commands you will type most often so muscle memory carries you. Pack the bag tonight: admit card, ID, pens, file, printed report.
Confirm the reporting time once more, plan the journey including a buffer for traffic, and set two alarms. Sleep properly; a candidate who blanks on a simple loop costs themselves more marks than one who skipped the last chapter.
Eat a real breakfast, reach the centre 30–45 minutes before reporting time, and keep your phone switched off and inside your bag before you enter the building. Do not carry your revision sheet into the lab.
Read the whole question paper or task list first. Identify the task you can complete most reliably and do that first to bank marks, then move to the harder one. Write your roll number on every sheet and every program file before writing anything else.
Write the program in small steps and run it after each step: load, inspect, clean, transform, model, evaluate, visualise. If a library fails, fall back to plain Python and say so in your answer. Add brief comments explaining each block - examiners award marks for readable, working code, not for clever uncommented code.
Keep a running record of what you did, the output you got, and any error you corrected; this becomes your observation sheet. In the viva, answer in short clear sentences, admit honestly when you do not know, and always relate the answer back to your own project or file.
Save your file with a sensible name containing your roll number, check that all output is visible or attached, number your pages, and hand over the answer script and file only when the examiner asks. Do not leave the lab with any written material issued to you.
Rules to check
Carry and display your admit card and school ID throughout the session. If either is missing, tell the examiner or centre coordinator immediately rather than waiting to be asked - [needs_review] confirm with the provider what alternative identification your centre accepts.
Mobile phones, smart watches, and any other connected device must be switched off and left outside the lab. Do not use them even to check syntax; treat a phone in the lab as you would in the theory examination.
Work only on the machine or workstation allotted to you. Do not copy another candidate's code, do not share your screen or files, and do not let anyone else type on your keyboard. Unattributed identical submissions are usually penalised for both candidates.
Ask the examiner before installing software, opening the internet, or using an unfamiliar library or dataset. Some centres forbid network access entirely; a quick question protects you from an allegation of malpractice.
Breaks, water, and toilet visits are normally permitted one candidate at a time with the invigilator's knowledge. Signal quietly and wait; do not simply walk out of the lab mid-task.
Marks in a practical are typically split between the written or programming task, the practical file or project, and the viva, and are awarded by an examiner present on the day. [needs_review] Confirm the exact split and pass requirements for this subject with your school, since WBCHSE weightings can change between sessions.
Submit exactly what is asked for - answer script, program file, and file or report - and leave the lab only after the examiner confirms your submission is recorded. Keep your own note of what you submitted.
If a machine or library fails through no fault of yours, report it at once and ask for a replacement or for the failure to be noted on your script. Do not spend twenty silent minutes troubleshooting hardware; the examiner can only credit a problem they know about.