Viva voce exams test more than memory. Examiners want to see how you think, how you work, and whether they can trust your results. If you prepare answers to the 20 questions below, you will cover the core of almost every practical viva, from lab sciences and engineering to computing, nursing, and pharmacy.
How Examiners Think
Examiners look for five things: purpose, logic, method, evidence, and limits. They ask “why” questions to probe understanding, not to trick you. They reward clear reasoning, safe practice, correct calculations, realistic error analysis, and honest limits. If you show you can repeat the work and improve it, you score high because it proves mastery, not memorization.
A Simple Answer Framework
Use this structure to keep answers tight and complete:
- Context: What you aimed to do.
- Logic: Why the method fits the goal.
- Execution: Key steps, controls, and safety.
- Analysis: How you processed data and judged quality.
- Limits: Errors, assumptions, and improvements.
This works because it mirrors the examiner’s marking map: clarity, correctness, and judgment.
The Top 20 Viva Questions (with answer patterns and examples)
What was the aim of your experiment/project?
Why they ask: To see if you can define the problem clearly.
Answer skeleton:
- State the objective in one line.
- Add the measurable outcome or criterion for success.
Example: “To determine the vitamin C content of orange juice using iodine titration, reported as mg/100 mL with ±5% uncertainty.”
What principle or theory does it rely on?
Why they ask: Ensures you understand the mechanism, not just steps.
Answer skeleton:
- Name the principle.
- State the key relationship or reaction.
- Note assumptions or valid conditions.
Example: “Acid–base titration: at equivalence, moles acid = moles base. Assumes complete reaction and accurate endpoint detection.”
Why did you choose this method over alternatives?
Why they ask: Tests judgment and awareness of trade-offs.
Answer skeleton:
- Compare 1–2 alternatives on accuracy, speed, cost, or safety.
- Tie choice to your objective and constraints.
Example: “I used colorimetric assay over HPLC because it met our ±5% target, used available reagents, and finished within a 2-hour lab.”
Walk me through your setup or protocol.
Why they ask: Checks whether you truly performed the work and understand critical points.
Answer skeleton:
- List major stages (prep, run, measure, clean).
- Highlight critical parameters (time, temperature, voltages).
- Mention any checkpoints or controls.
Example: “Prepared standard curve (0–50 mg/L). Warmed spectrophotometer 20 minutes. Zeroed at 540 nm. Measured samples in triplicate.”
What were your variables and controls?
Why they ask: Valid conclusions need comparison.
Answer skeleton:
- Independent, dependent, controlled variables.
- Positive/negative controls or baseline.
Example: “Independent: enzyme concentration. Dependent: reaction rate (ΔA/min). Controlled: pH 7.4, 25°C, substrate at Km. Negative control: no enzyme.”
How did you calibrate or standardize your instruments?
Why they ask: Calibration is the root of trustworthy data.
Answer skeleton:
- Method of calibration or standard curve.
- Acceptance criteria (R², slope tolerance, zero check).
Example: “pH meter two-point calibration at 4.00 and 7.00. Acceptable slope 58–60 mV/decade. Rechecked drift after 30 minutes.”
What safety or ethical measures did you follow?
Why they ask: Safety and ethics are core competencies.
Answer skeleton:
- PPE, hazard controls, waste disposal.
- Consent/privacy (for human data) or humane handling (for animals).
Example: “Used gloves and fume hood with organic solvents. Labeled halogenated waste. For patient data, removed identifiers and stored with access control.”
How did you ensure reliability and validity?
Why they ask: They want repeatable and accurate results.
Answer skeleton:
- Replicates, randomization, blinding (if relevant).
- Precision metrics (SD, %RSD) and accuracy checks (recovery/spike).
Example: “Triplicates gave %RSD 2.3%. Spike recovery was 97–102%, within our 95–105% target.”
Which formulas or models did you use, and what are the assumptions?
Why they ask: Assumptions limit conclusions.
Answer skeleton:
- State the formula/model.
- Key assumptions and when they fail.
Example: “Ohm’s law (V = IR) assumes linear, temperature-stable resistor. At high current, heating breaks linearity.”
Show a sample calculation or your data processing pipeline.
Why they ask: Errors often hide in arithmetic or code.
Answer skeleton:
- Pick one data point and walk through steps.
- Include units and rounding rules.
Example: “Concentration = (Abs − b)/m. For Abs 0.420, slope 0.010 A/(mg/L), intercept 0.020 → (0.420−0.020)/0.010 = 40 mg/L.”
What is your main result, and what does it mean?
Why they ask: See if you can interpret, not just calculate.
Answer skeleton:
- Report the key figure with uncertainty.
- Compare with expected/reference and explain significance.
Example: “Yield was 78% ±3%. Literature reports 80–85%. Slightly lower, likely due to product loss during filtration.”
What are the main sources of error or uncertainty? Quantify them.
Why they ask: Good scientists measure doubt.
Answer skeleton:
- Instrumental, methodological, and human errors.
- Approximate contribution of each (dominant first).
- How you reduced them.
Example: “Pipette tolerance ±1% dominates. Reading endpoint adds ±0.5%. Combined ~±1.1% by root-sum-of-squares.”
Your results differ from theory. Why?
Why they ask: Tests your diagnostic thinking.
Answer skeleton:
- List plausible, ranked causes.
- State quick checks to isolate each cause.
Example: “Lower enzyme rate could be pH drift, inhibitor contamination, or outdated substrate. I’d verify pH, run a blank with fresh substrate, and inspect storage logs.”
What are the limitations of your approach?
Why they ask: Honest limits show maturity.
Answer skeleton:
- Range, sensitivity, selectivity, time, cost.
- Where it fails and what that implies for conclusions.
Example: “Colorimetry loses linearity above 50 mg/L, so high samples need dilution. Matrix color can bias absorbance.”
How would you improve or extend this work?
Why they ask: Looks for forward thinking and feasibility.
Answer skeleton:
- One practical fix (reduces error or time).
- One extension (new data or capability).
Example: “Automate endpoint detection to cut operator bias. Extend with HPLC validation on 10% of samples.”
If you had half the time, what would you keep or drop?
Why they ask: Prioritization under constraints.
Answer skeleton:
- Keep steps that protect validity (controls, calibration).
- Drop non-critical repeats or decorative plots.
Example: “Keep triplicate standards and blank. Reduce sample replicates from 3 to 2 while monitoring %RSD.”
How does this connect to real-world use?
Why they ask: Application shows depth and relevance.
Answer skeleton:
- Name a concrete setting and stakeholder.
- State impact, benefit, or risk.
Example: “Calibrated glucometers reduce dosing errors in clinics. A 5% bias could cause hypoglycemia in insulin titration.”
Explain your work to a non-specialist.
Why they ask: Clarity reveals true understanding.
Answer skeleton:
- One-sentence summary without jargon.
- Simple analogy.
Example: “We checked how much vitamin C is in juice by adding a chemical that reacts only with vitamin C, like counting coins by matching each with a token.”
What common misconception do students have here?
Why they ask: Spotting traps means you’ve moved past them.
Answer skeleton:
- Name the misconception.
- Correct it with a short reason or example.
Example: “People think the pH at equivalence is always 7. Not true for weak acid–strong base titrations; the conjugate base shifts pH above 7.”
Given this change in a variable, predict the outcome and justify.
Why they ask: Tests transfer of knowledge, not rote memory.
Answer skeleton:
- State direction and magnitude qualitatively.
- Reference the governing principle.
- Mention limits where the relationship breaks.
Example: “Doubling sample rate in a sensor reduces aliasing per Nyquist. Benefit stops once rate exceeds twice the highest signal frequency; noise and data size then dominate.”
What is your takeaway learning from this practical?
Why they ask: Reflection shows you can generalize and improve.
Answer skeleton:
- State one technical insight.
- State one process or teamwork insight.
Example: “I learned endpoint detection benefits from objective criteria. I also learned to log parameter changes immediately to aid troubleshooting.”
Model Mini-Answers Across Fields
- Chemistry (Titration): “Aim: determine acetic acid % in vinegar. Principle: moles acid = moles base at equivalence. Calibrated burette with distilled water; corrected for meniscus. Used phenolphthalein; blank titration for CO₂ effect. Result 4.8% ±0.1%, close to label 5%. Main error from endpoint color subjectivity; would use pH probe next time.”
- Electronics (Op-amp Amplifier): “Aim: design non-inverting amp with gain 11. Theory: Av = 1 + Rf/Rg. Measured input/output with 1 kHz sine; ensured no clipping at ±12 V rails. Calibrated scope probes. Result Av = 10.9, bandwidth ~120 kHz. Limitation: slew-rate induced distortion above 50 kHz. Improvement: use higher slew-rate op-amp.”
- Programming (Sorting Benchmark): “Aim: compare quicksort vs mergesort on 1e6 random ints. Principle: average O(n log n); cache behavior differs. Controlled compiler flags, fixed seed, repeated runs (n=5). Measured median time; 95% CI. Quicksort faster by 12% due to better cache locality. Limitation: worst-case inputs not tested.”
- Nursing (Vital Signs): “Aim: measure accurate BP. Principle: occlusion and Korotkoff sounds. Controlled cuff size, arm at heart level. Took two readings; averaged if within 5 mmHg. Educated patient to avoid caffeine 30 min prior. Result 128/82 mmHg. Limitation: white-coat effect; recommend ambulatory monitoring if elevated.”
How Examiners Award Marks
- Accuracy: Correct theory, units, and calculations.
- Method: Clear steps, proper controls, and calibration.
- Safety/Ethics: Hazards named, mitigations applied.
- Analysis: Uncertainty quantified; sensible comparisons.
- Communication: Concise, structured, confident, honest.
Marks drop when answers are vague, unquantified, or overconfident. They rise when you show evidence, numbers, and judgment.
Rapid Prep Checklist (15 Minutes Before Viva)
- Write your one-line aim and one-line main result with uncertainty.
- List three critical parameters and their set values.
- Sketch the control(s) and calibration method in your head.
- Note top two error sources with rough percentages.
- Prepare one improvement and one real-world application.
Delivery Tips That Add Marks
- Lead with the answer, then justify. It shows clarity and saves time.
- Use numbers. “R² = 0.998” beats “good fit.” Numbers prove claims.
- Admit uncertainty fast. Follow with how you would test or fix.
- Keep units consistent. Unit errors signal shaky basics.
- Pause, don’t ramble. Short sentences help you think and the examiner follow.
Prepare these 20 answers using the framework above, and practice saying them out loud. You will sound clear, competent, and in control—the exact qualities examiners reward with top marks.
I am a Registered Pharmacist under the Pharmacy Act, 1948, and the founder of PharmacyFreak.com. I hold a Bachelor of Pharmacy degree from Rungta College of Pharmaceutical Science and Research. With a strong academic foundation and practical knowledge, I am committed to providing accurate, easy-to-understand content to support pharmacy students and professionals. My aim is to make complex pharmaceutical concepts accessible and useful for real-world application.
Mail- Sachin@pharmacyfreak.com