10th Class Computer Science — MCQs & Practice Questions

The core of the OS, permanently resident in memory, which manages the CPU, memory and devices and controls access to them. Other parts of the OS such as the user interface can be restarted or replaced; the kernel cannot.

Through time-slicing: the operating system gives each program a few milliseconds of CPU time in turn and switches between them far faster than a person can perceive. Only one instruction is ever executing, so the simultaneity is an illusion.

Any program that runs inherits the permissions of the account that started it. Malware launched from an administrator account can therefore alter system files and install itself permanently, whereas the same malware run from a standard account is confined to that user's own files.

1. Process management — scheduling which program uses the CPU and for how long[1]
2. Memory management — allocating RAM to each program and keeping them separate[1]
3. File management — organising storage into files and folders and controlling access to them[1]
4. Device management — communicating with hardware through drivers; also accept security and user accounts[1]

Process, memory, file and device management — plus security and the user interface.

1. RAM has filled, so the OS is moving pages of memory out to disk — paging or swappingthe constant disk light is the clue[1]
2. Disk access is thousands of times slower than RAM, so every switch between programs now waits on the diskthe speed gap is the explanation[1]
3. Solution: close unused applications and reduce the number of programs starting automatically[1]
4. Solution: install more RAM, which removes the need to page rather than merely making paging fasteran SSD helps but does not fix the cause[1]

RAM is full and the OS is paging to disk. Close programs, or add RAM.

1. Office machines: a single-user multi-tasking operating system, since one person at a time runs several applications togetheraccept multi-user for shared machines[1]
2. Patient monitor: a real-time operating system[1]
3. Real-time guarantees that a response occurs within a fixed maximum time, not merely quickly on averagethe guarantee is the distinguishing feature[1]
4. For a patient monitor an alarm raised late is as dangerous as no alarm, so a predictable worst case matters more than average speed[1]
5. File permissions grant read, write and execute rights separately to the owner, a group and others[1]
6. So clinical staff can be given read access to records while administrative staff cannot open them at all, and only authorised users may alter themthe example must fit the hospital[1]

(a) multi-tasking for the office, real-time for the monitor (b) a guaranteed maximum response time (c) separate read and write rights per group

A system restore point saves system files, settings and installed programs, and is used to undo a bad update or driver. A file backup copies the user's own documents. A restore point will not recover a deleted file, and a backup will not repair a broken operating system.

Safe mode starts the operating system with only essential drivers and no startup programs. If the machine is stable in safe mode, the fault must lie in one of the drivers or startup programs that safe mode did not load — which narrows the search considerably.

An SSD has no moving parts and reaches every location equally quickly, so there is no time to be saved. Defragmentation performs a very large number of writes, and each flash cell tolerates only a limited number, so it shortens the drive's life for no benefit.

1. Boot into safe mode, which loads a basic display driver instead of the new one[1]
2. Uninstall the new driver, or use Device Manager to roll it back to the previous version[1]
3. Alternatively use a system restore point created before the driver was installedthis is exactly what restore points are for[1]
4. Restart normally and confirm the machine boots; then install a driver version known to workverification is part of the process[1]

Safe mode, then roll back or uninstall the driver — or restore to a point from before the installation.

1. A system image is a complete copy of the entire drive, including the operating system, programs and settings[1]
2. A file backup copies only the user's chosen documents and data[1]
3. Use an image after a total disk failure or major corruption, so the machine is restored exactly without reinstalling everything[1]
4. Use file backups frequently, since documents change daily, whereas an image is large and slow and is made occasionallythe frequency point is the practical insight[1]

An image restores the whole machine; a backup restores the documents. Image occasionally, files frequently.

1. Automatic daily file backups of documents, so no more than one day's work is ever lostautomatic, not left to the user[1]
2. Periodic system images so a failed machine can be restored without reinstalling everything[1]
3. Follow the 3-2-1 rule — three copies, two media types, one off-site or in the cloud — and test a restorean untested backup is an assumption[1]
4. A second partition is on the same physical disk, so a disk failure destroys both partitions at oncethe key point[1]
5. It also offers no protection against theft, fire or ransomware, all of which reach everything on the machine[1]
6. Disk health monitoring reports the drive's own error counters and often warns of impending failure days in advanceaccept regular disk checks[1]

(a) daily automatic file backups plus periodic images, following 3-2-1 and tested (b) a partition shares the physical disk, so both die together (c) disk health monitoring

A variable is a named location in memory holding a value. x = 10 creates the name x and assigns the integer value 10 to it. Assigning again later replaces the value rather than creating a second variable.

Because input() always returns a string, even when the user types digits. Without conversion, + would join the text instead of adding — "5" + "3" gives "53" — and comparisons would compare text rather than numeric value.

= is the assignment operator, storing a value in a variable. == is the comparison operator, testing whether two values are equal and producing True or False.

1. length = float(input("Enter length: "))float rather than int allows decimal measurements[1]
2. width = float(input("Enter width: "))[1]
3. area = length * widtha meaningful variable name is expected[1]
4. print("The area is", area)output must be labelled, not a bare number[1]

Read both values as floats, multiply, and print with a label.

1. (i) 3 — integer division discards the remaindernot 3.5[1]
2. (ii) 1 — the remainder when 7 is divided by 2[1]
3. (iii) 3.5 — / always produces a floatnote the decimal point[1]
4. (iv) 8 — ** is the power operator2 cubed[1]

(i) 3 (ii) 1 (iii) 3.5 (iv) 8

1. Error 1: the inputs are strings, so they are joined rather than addedint() or float() is missing[1]
2. Error 2: operator precedence — a + b / 2 divides b by 2 first, so brackets are needed: (a + b) / 2this error survives even after the type is fixed[1]
3. Corrected input lines using float(input(…))[1]
4. avg = (a + b) / 2 followed by a labelled print[1]
5. Test with normal data, such as 60 and 80, and check the answer is 70a value you can verify by hand[1]
6. Test with boundary data such as 0 and 100, and with erroneous data such as text, to see how it behavesall three kinds of test data[1]

(a) inputs not converted, and missing brackets (b) float(input(…)) and (a+b)/2 (c) normal, boundary and erroneous data

Sequence — statements written one after another, needing no keyword. Selection — if. Iteration — for or while.

When the number of repetitions is not known in advance and depends on a condition evaluated during the loop — for example repeating until the user enters valid input or types "quit". A for loop is used when the count is fixed beforehand.

Five times, with i taking the values 2, 3, 4, 5 and 6. The upper bound 7 is where the sequence stops and is not itself used.

1. for n in range(1, 21): — the upper bound must be 21 to include 20the off-by-one is examined here[1]
2. if n % 3 == 0:the remainder test for divisibility[1]
3. print(n) correctly indented inside the ifindentation is part of the mark[1]
4. Output: 3, 6, 9, 12, 15, 18[1]

A for loop over range(1, 21) with an if n % 3 == 0 test inside.

1. Nothing inside the loop changes count[1]
2. So the condition count > 0 is true on every pass and never becomes false — an infinite loopnaming it as infinite is expected[1]
3. Add count = count - 1 inside the loopaccept count -= 1[1]
4. It must be indented inside the loop; placed outside it would run only once, after the loopthe indentation point is a mark[1]

The loop variable is never decremented. Add count = count - 1 indented inside the loop.

1. A for loop, because the number of marks is known in advance to be tenthe reason must be given[1]
2. Initialise total = 0 and passes = 0 before the loopaccumulators must start outside the loop[1]
3. for i in range(10): then read a mark and add it to total[1]
4. if mark >= 50: passes = passes + 1, correctly indented inside the loop[1]
5. After the loop: print(passes) and print(total / 10)[1]
6. The prints must be outside the loop, because inside they would run on every pass and print ten partial results instead of one final answerthe reason is the mark[1]

(a) for, since ten is known (b) accumulators before, loop reads and tests, prints after (c) outside, or they print ten partial results

Data is raw unprocessed facts with no context, such as the numbers 45, 62, 71. Information is data that has been given meaning — those are three students' marks out of 100 in a physics test.

Advantage: it already exists, so it is quick and cheap to obtain and often covers a far larger population than you could survey. Disadvantage: it was collected for a different purpose, so it may be out of date or may not exactly answer your question, and you cannot verify how carefully it was gathered.

Bias occurs when the sample is not representative of the population, so certain groups are over- or under-represented and the results are systematically wrong. For example, surveying only smartphone owners about internet access excludes exactly the people whose answers would differ.

1. Define the question or problem to be answeredcollecting before deciding what you want to know produces unusable data[1]
2. Collect the data from appropriate primary or secondary sources[1]
3. Clean it — remove duplicates, correct errors and handle missing valuesthe stage most often omitted[1]
4. Analyse it, then present the findings in a suitable visual form[1]

Define the question, collect, clean, analyse, present.

1. The first students to arrive are not typical — they may live nearby, travel by a particular route, or be in certain year groups[1]
2. Their views on the canteen may differ systematically from those of students who arrive later, so the sample is unrepresentativethe link to unrepresentativeness is the mark[1]
3. A stratified sample would divide students by year group and sample each in proportion to its sizeaccept random sampling from the full roll[1]
4. This ensures every year group is represented in the correct proportion, so the results reflect the whole schoolthe justification is required[1]

(a) early arrivals are not typical of the school (b) stratified sampling by year group, so each is represented proportionally

1. A sensor records continuously and automatically, so no readings are forgotten and none are affected by human error in reading a scale[1]
2. It can record far more frequently than a person could, and at times when nobody is present[1]
3. The 400 °C values are clearly impossible and are sensor errors, so they should be removed or marked as invalid rather than analysedidentifying them as outliers or errors[1]
4. The missing days should be recorded as missing, not as zero — entering 0 °C would drag the mean down and invent a cold spell that never happenedthis is the key insight[1]
5. A line graph, with date on the horizontal axis and temperature on the vertical[1]
6. Because the data is continuous and ordered in time, a line graph shows the trend and the seasonal pattern that separate bars would obscurethe justification is the mark[1]

(a) continuous automatic recording, no human error (b) discard the impossible values; mark the gaps as missing, never as zero (c) a line graph, because the data is continuous over time

Artificial intelligence is the broad field of making machines perform tasks that would ordinarily require human intelligence. Machine learning is a subset of AI in which the system learns patterns from data rather than being given explicit rules. All machine learning is AI; not all AI is machine learning.

Learning from data in which each example is labelled with the correct answer. The model learns to reproduce those labels on new data. Example: training on photographs already tagged "cat" or "dog" so the system can classify unseen photographs.

Because a model can score perfectly on data it has effectively memorised, which says nothing about how it will behave on new examples. Only unseen data measures whether it has learned the general pattern — the property that actually matters in use.

1. Supervised: trained on labelled data — for example spam detection from emails already marked spam or not[1]
2. Unsupervised: trained on unlabelled data and finds structure itself — for example grouping customers into segments[1]
3. Reinforcement: learns by acting and receiving rewards or penalties — for example a program learning to play a game[1]
4. The distinguishing factor is what the training data contains: answers, no answers, or a reward signalthe unifying statement earns the fourth mark[1]

Labelled data, unlabelled data, and reward feedback respectively.

1. The model learns the patterns in the historical decisions, including any that were unfair[1]
2. If certain groups were refused disproportionately in the past, the model reproduces that pattern while appearing objectivethe appearance of objectivity is part of the harm[1]
3. Reduce it by examining the training data for representativeness, and testing outcomes separately for each group[1]
4. And by keeping a human decision-maker for consequential decisions, with a documented route to appealaccept requiring the model to be explainable[1]

The model learns historical unfairness. Audit the data, test outcomes per group, and keep human oversight.

1. Historical records of past students — attendance, assessment marks, homework completion — each labelled with whether that student ultimately passed or failedlabelled data, so this is supervised learning[1]
2. The data is split into a training set and a test set; the model learns from the first and is measured on the second[1]
3. Unfairness: if the historical data reflects a group that was under-supported in the past, the model predicts failure for that group and the prediction becomes self-fulfilling[1]
4. Or: a feature that correlates with background rather than ability — such as distance travelled to school — drives the predictioneither example accepted[1]
5. A prediction is a statistical likelihood about a group, not a fact about the individual student in front of the teacher[1]
6. The model cannot explain its reasoning, and being labelled "likely to fail" could itself affect how a student is treated and how they see themselves[1]

(a) labelled historical records, split into training and test sets (b) it reproduces past under-support as a prediction (c) a prediction is about likelihood, not about the individual, and cannot justify itself

AI can analyse medical images such as chest X-rays and flag those most likely to show disease, so a radiologist reviews the urgent cases first. The benefit is speed and consistency: it works continuously without fatigue, letting a small number of specialists cover a large population.

It usually cannot explain how it reached a decision, so a person affected cannot be told why. And it reproduces the biases in its training data while appearing objective, which can make unfair outcomes harder to challenge.

A farmer can photograph an affected leaf with a phone and a model identifies the crop disease and suggests treatment. This reaches farms that an agricultural extension officer could not visit often enough, and gives an answer within seconds rather than days.

1. Benefit: they do not tire, become distracted or drive under the influence, and human error causes most collisions[1]
2. Benefit: they can give independent mobility to people unable to drive, and can coordinate to reduce congestion[1]
3. Risk: an unfamiliar situation not represented in training may be handled badly, and roads present many such situations[1]
4. Risk: responsibility for a collision is unclear — the owner, the manufacturer or the software developer — and the vehicle is a target for interferenceaccept job losses among professional drivers[1]

Benefits: no fatigue or distraction, and mobility for non-drivers. Risks: unfamiliar situations, and unclear liability.

1. There are millions of transactions, far more than any human team could examine[1]
2. Fraud shows as an unusual pattern, and finding patterns in large volumes of data at speed is exactly what these systems do well[1]
3. Problem: false positives — a legitimate but unusual transaction is blocked, such as a card used abroad on holiday[1]
4. The customer is inconvenienced and may not be told why, since the system cannot explain the pattern it reacted tothe inability to explain is the sharper point[1]

The volume and the pattern-matching suit AI; the cost is blocked legitimate transactions with no explanation.

1. In favour: decisions would be made far faster and applicants would wait less[1]
2. In favour: the same rules would be applied to every applicant, removing the inconsistency between individual assessors[1]
3. Against: trained on past decisions, it would reproduce any historical unfairness while appearing objective[1]
4. Against: an applicant refused assistance could not be told why, and would have no meaningful basis for appealthe accountability point[1]
5. Recommendation: use the system to sort and prioritise applications, with a human assessor making every final decision[1]
6. Justified because the speed and consistency are gained while accountability and the right to an explanation are preserved, and unusual cases still reach a personthe justification must address both sides[1]

(a) speed and consistency (b) inherited bias and no explanation (c) AI prioritises, humans decide — keeping the benefit and the accountability

Identifying a problem and building a business that solves it using digital tools and platforms — cloud services, online marketplaces, digital payments — rather than traditional premises and infrastructure. The entrepreneur takes the risk in exchange for the potential reward.

Freemium offers a basic version free and charges for an upgraded one. The risk is that free users still cost money to serve while only a small percentage ever upgrade, so the business can grow its user numbers and its losses at the same time.

The smallest version of a product that still delivers real value to a real user and generates feedback. Its purpose is to test the central assumption cheaply, before the full product is built.

1. An existing online marketplace or social media page provides an audience and a shopfront[1]
2. So there is no need to build a website or pay for premises before the first salethe explanation is the mark, not the naming[1]
3. A digital payment gateway accepts card and mobile paymentsaccept cloud hosting or free design tools[1]
4. So the seller can take money from anywhere without setting up merchant infrastructure of their own[1]

A marketplace supplies audience and shopfront; a payment gateway supplies the ability to take money.

1. Profit = revenue − costs = 400 000 − 350 000[1]
2. = Rs 50 000 per monththe business is profitable[1]
3. Money leaves the business immediately for wages but arrives 60 days later from customers[1]
4. So the account can be empty even though the business is profitable — this is a cash flow problem, and it can force closurenaming cash flow is expected[1]

Profit Rs 50 000/month, but a 60-day gap between paying costs and receiving revenue creates a cash flow problem.

1. Talk to farmers first to establish that identifying diseases is genuinely a problem they face and currently handle badlyvalidating the assumption[1]
2. Build an MVP where farmers send a photograph by WhatsApp and an agricultural expert replies manuallydelivers the value with no app[1]
3. Measure whether farmers use it repeatedly and whether they will pay, before writing any software[1]
4. A freemium or low-cost subscription model: a few free identifications, then a small monthly feeaccept any justified model[1]
5. Justified because the value is ongoing across a growing season, and farmers may be unwilling to pay before seeing it workthe justification must fit the customer[1]
6. Risks: farmers may have limited smartphone access or connectivity in rural areas; a wrong identification could destroy a crop and the business's reputationtwo risks needed[1]

(a) interview farmers, then run it manually over WhatsApp (b) freemium or low subscription, since value is ongoing (c) rural connectivity, and the consequences of a wrong diagnosis