Question 1

Digital reliability principle: Computer ICs operate reliably because digital logic is based on a design paradigm that uses _____ states (levels) rather than many analog levels.

Accepted Answer

Introduction / Context: Digital integrated circuits achieve robustness by encoding information with a small number of discrete levels—most commonly two: logic 0 and logic 1. This binary (two-state) approach provides noise margins and regenerative switching, which together make digital systems tolerant of moderate disturbances and component variation. Given Data / Assumptions: Digital ICs use Boolean logic with well-defined thresholds. The question highlights the reliability benefit of discrete states. Two-state design underpins error-resistant computation and storage. Concept / Approach: By separating valid logic levels with thresholds and hysteresis (in some gates), digital circuits filter small analog variations. Transistors switch between saturation/cutoff or strong/weak inversion regions to produce rail-to-rail outputs. As long as signals remain within noise margins, computation remains correct despite supply variation, crosstalk, or temperature drift. Step-by-Step Solution: Recognize that binary encoding = two discrete states.Link discrete states to noise immunity and reliable switching.Select “Two-states” as the defining reliability paradigm. Verification / Alternative check: Specifications list VOH/VOL and VIH/VIL levels and define noise margins ensuring predictable logic behavior. Why Other Options Are Wrong: “Top-bottom,” “system,” and “two-stage” do not describe the fundamental binary nature of digital logic. Common Pitfalls: Confusing “two-stage” amplifier topology with “two-state” logic; they are unrelated concepts. Final Answer: Two-states.

Question 2

Binary arithmetic — addition: Compute 1101₂ + 1010₂ and select the correct binary sum.

Accepted Answer

Introduction / Context: Binary addition follows the same carry rules as decimal addition but with base 2 digits. Mastery of bit-wise addition is essential for digital design, arithmetic logic units, and low-level programming. Given Data / Assumptions: Addend A = 1101₂ (decimal 13). Addend B = 1010₂ (decimal 10). We compute the exact binary sum. Concept / Approach: Use columnar addition from least significant bit (rightmost) to most significant bit, carrying when a column sums to 2 (10₂) or 3 (11₂). The process mirrors full-adder logic: sum bit = A ⊕ B ⊕ carry_in, carry_out = majority(A, B, carry_in). Step-by-Step Solution: Align bits: 1101 + 1010.LSB: 1 + 0 = 1, carry 0 → sum bit 1.Next: 0 + 1 = 1, carry 0 → sum bit 1.Next: 1 + 0 = 1, carry 0 → sum bit 1.MSB: 1 + 1 = 10₂ → sum bit 0, carry 1.Write final: carry 1 then 0 1 1 1 → 10111₂. Verification / Alternative check: Convert to decimal: 13 + 10 = 23. Convert 10111₂ to decimal: 16 + 4 + 2 + 1 = 23. Matches. Why Other Options Are Wrong: 10101₂ = 21; 11000₂ = 24; 11011₂ = 27; none match 23. Common Pitfalls: Forgetting the final carry; misaligning bits when adding numbers of equal length can still produce mistakes if carry is mishandled. Final Answer: 10111₂.

Question 3

Binary word range — 6-bit maximum value: What is the largest unsigned integer value representable by a 6-bit binary word?

Accepted Answer

Introduction / Context: Bit width limits the numeric range of unsigned binary values. In general, an n-bit unsigned word represents integers from 0 to 2^n − 1. This is foundational for register sizing, counter design, and memory addressing. Given Data / Assumptions: The word is 6 bits wide. Unsigned representation is assumed. We seek the maximum value. Concept / Approach: For n bits, the count of distinct states is 2^n. The maximum unsigned integer is all bits set to 1. Therefore, with n = 6, max = 2^6 − 1. Step-by-Step Solution: Compute 2^6 = 64.Subtract 1 to get the maximum: 64 − 1 = 63.Confirm binary: 111111₂ corresponds to decimal 63. Verification / Alternative check: List endpoints: min = 0 (000000₂), max = 63 (111111₂) → 64 distinct values as expected. Why Other Options Are Wrong: 36 and 65 are not endpoints for 6 bits; 64 would require 7 bits for unsigned max. Common Pitfalls: Confusing “number of states” (64) with “maximum value” (63). Final Answer: 63.

Question 4

Adders — hardware count: An m-bit parallel binary adder (ripple or look-ahead per bit) contains how many full adder stages?

Accepted Answer

Introduction / Context: Parallel adders implement multi-bit addition by cascading single-bit full adders. Each stage handles a bit position and propagates/produces carry signals. Knowing the stage count is essential for estimating delay, area, and power. Given Data / Assumptions: We consider an m-bit adder for two m-bit operands. Each bit requires sum and carry processing. Half adders are insufficient when a carry-in must be accommodated. Concept / Approach: A full adder computes sum and carry from A_i, B_i, and carry_in. Since every bit position has these three inputs (except perhaps the LSB if designed with a half adder when carry_in is assumed 0), the canonical design uses m full adders to allow a carry chain across all bits. Ripple, carry-look-ahead, or other schemes still allocate one full-adder cell per bit. Step-by-Step Solution: Map one full-adder cell to each of the m bit positions.Account for carry propagation across all stages.Therefore, total full adders = m. Verification / Alternative check: Logic diagrams of 4-bit adders show 4 full adders; extending to m bits generalizes linearly. Why Other Options Are Wrong: (m+1) and (m−1) miscount; m/2 would only cover half the bits. Common Pitfalls: Using a half adder at LSB is a special case only when carry_in is guaranteed 0; the general parallel adder uses m full adders. Final Answer: m full adders.

Question 5

Number system conversion — octal to hexadecimal: Convert the octal number 136₈ to its hexadecimal equivalent and choose the correct answer.

Accepted Answer

Introduction / Context: Efficient conversion between octal, binary, and hexadecimal relies on grouping bits because bases 8 and 16 are powers of 2. Octal digits map to 3-bit groups; hexadecimal digits map to 4-bit groups. This allows quick mental or manual conversion without decimal intermediate steps. Given Data / Assumptions: Number to convert: 136₈. We aim to produce an exact hexadecimal result. No calculators are required; bit grouping is used. Concept / Approach: Map each octal digit to a 3-bit binary group, concatenate, then regroup into 4-bit nibbles for hexadecimal. Add leading zeros if necessary to complete the leftmost nibble—this does not change the value. Step-by-Step Solution: Write octal to binary: 1 → 001, 3 → 011, 6 → 110.Concatenate: 001 011 110 → binary 001011110.Pad on the left to a multiple of 4 bits: 0000 0101 1110.Translate nibbles: 0000 → 0, 0101 → 5, 1110 → E.Drop leading 0: result = 5E₁₆. Verification / Alternative check: Decimal check: 136₈ = 164 + 38 + 6 = 64 + 24 + 6 = 94₁₀. Hex 5E₁₆ = 5*16 + 14 = 80 + 14 = 94₁₀. Matches. Why Other Options Are Wrong: 7E₁₆ (126₁₀), 5A₁₆ (90₁₀), and 5D₁₆ (93₁₀) do not equal 94₁₀. Common Pitfalls: Forgetting to pad the leftmost group to 4 bits or mis-grouping from the right instead of from the left for hex conversion. Final Answer: 5E₁₆.

Question 6

Complements — bitwise inversion: What is the 1’s complement (bitwise NOT) of the 16-bit value 0000 1111 0010 1101₂?

Accepted Answer

Introduction / Context: The 1’s complement of a binary word is formed by inverting each bit: 0 → 1 and 1 → 0. This operation is used in low-level arithmetic, checksum algorithms, and digital logic. Correct grouping into nibbles or bytes helps avoid transcription errors. Given Data / Assumptions: Original word: 0000 1111 0010 1101 (16 bits). Operation: bitwise inversion (1’s complement). Spacing is only for readability; it does not affect values. Concept / Approach: Invert each bit independently across all positions. For grouped readability, confirm each nibble inversion using hex if desired: 0x0 → 0xF, 0xF → 0x0, 0x2 → 0xD, 0xD → 0x2. This provides a quick cross-check. Step-by-Step Solution: Start: 0000 → 1111.Next: 1111 → 0000.Next: 0010 → 1101.Next: 1101 → 0010.Combine: 1111 0000 1101 0010. Verification / Alternative check: Hex route: 0000 1111 0010 1101₂ = 0x0F2D; 1’s complement → 0xF0D2 → 1111 0000 1101 0010₂. Matches. Why Other Options Are Wrong: Option A inverts only some nibbles. Options C and D do not correspond to full bitwise inversion of the given word. Common Pitfalls: Accidentally computing 2’s complement (invert + add 1) instead of 1’s complement; mis-grouping bits when copying. Final Answer: 1111 0000 1101 0010.

Question 7

Adder architectures — carry performance: In which adder design is the conventional ripple carry delay effectively eliminated by computing carries in parallel?

Accepted Answer

Introduction / Context: Adder speed is often limited by how quickly carry signals propagate from the least significant bit to the most significant bit. Ripple-carry adders pass each carry sequentially, leading to linear delay with bit-width. Faster architectures precompute carry signals using generate/propagate logic to reduce or eliminate ripple delay. Given Data / Assumptions: We compare common adder types by carry behavior. Goal: identify the design that removes linear ripple bottlenecks. Basic knowledge of generate (G) and propagate (P) concepts is assumed. Concept / Approach: A carry-look-ahead (CLA) adder forms carry outputs as Boolean functions of operand bits and the initial carry, using propagate (P = A ⊕ B) and generate (G = A * B) signals. Closed-form expressions compute c1, c2, …, cN in parallel or in grouped blocks, dramatically reducing delay compared to ripple chains. Step-by-Step Solution: Recognize ripple adders have O(n) carry delay.Identify CLA as using parallel carry equations (e.g., c2 = G1 + P1*c1, etc.).Select “Carry-look-ahead adder” as the architecture eliminating ripple delay. Verification / Alternative check: Timing comparisons in digital design texts show CLA and its variants (carry-skip, carry-select, parallel prefix) outperform ripple adders by shortening the critical path. Why Other Options Are Wrong: Half and full adders are single-bit cells, not multi-bit architectures. “Parallel adder” generally means a multi-bit ripple structure unless carry acceleration is specified. Common Pitfalls: Equating any “parallel adder” with fast carry; without look-ahead/skip/select, it still ripples. Final Answer: Carry-look-ahead adder.

Question 8

In digital electronics, convert the following octal value to binary. What is the correct binary representation of 743₈ (octal 743)?

Accepted Answer

Introduction / Context: Base conversion is a core skill in digital electronics. Octal (base 8) and binary (base 2) are closely related because each octal digit maps exactly to three binary bits. This question asks you to convert the octal number 743₈ into its binary equivalent without changing the numeric value, only the representation. Given Data / Assumptions: Given number: 743 in base 8 (octal). Target: binary (base 2) representation. No arithmetic value change; only base conversion. Concept / Approach: Each octal digit corresponds to a 3-bit binary group: 0→000, 1→001, 2→010, 3→011, 4→100, 5→101, 6→110, 7→111. Therefore, convert each digit independently and then concatenate the groups from most significant to least significant. Leading zeros (if any) on the highest group may be omitted in the final binary string for compactness. Step-by-Step Solution: Map 7₈ → 111₂.Map 4₈ → 100₂.Map 3₂ → 011₂.Map 2₈ → 010₂.Concatenate: 111 100 011 010 → 111100011010₂. Verification / Alternative check: Alternative via decimal: 78^3 + 48^2 + 38 + 2 = 7512 + 4*64 + 24 + 2 = 3584 + 256 + 26 = 3866₁₀. Now convert 3866₁₀ to binary to confirm it is 111100011010₂. Both methods agree. Why Other Options Are Wrong: 1111000110111₂ and 110011010111₂ change one or more 3-bit groups, altering the value. 111111111000₂ represents a different decimal value altogether. “None of the above” is incorrect because a correct mapping exists. Common Pitfalls: Mixing up the 3-bit group for 3 (011) or 4 (100); skipping a digit; forgetting that each octal digit always maps to exactly three binary bits. Final Answer: 1111000110102

Question 9

NAND gate behavior check: For a NAND gate (AND followed by inversion), all inputs must be _____ to produce a LOW (0) output.

Accepted Answer

Introduction / Context: NAND is one of the universal logic gates. Understanding its truth behavior is essential because NAND gates can implement any combinational logic. Since a NAND is an AND followed by an inverter, it produces the complement of an AND output. Given Data / Assumptions: NAND = NOT(AND(inputs)). We are interested in the condition that yields a LOW (0) at the output. Input signals are standard binary logic levels (0 for LOW, 1 for HIGH). Concept / Approach: An AND gate produces 1 only when all inputs are 1 (HIGH). The NAND inverts that: output is 0 only when the AND would be 1, i.e., when all inputs are HIGH. In every other case (at least one input LOW), AND yields 0 and NAND yields 1. Step-by-Step Solution: Recognize AND truth: all HIGH → 1; otherwise → 0.Apply inversion: NAND output = NOT(AND output).Therefore, NAND output is LOW only when all inputs are HIGH. Verification / Alternative check: Create a simple two-input truth table; extend to N inputs. For inputs (1,1), AND=1 → NAND=0. For any input set containing a 0, AND=0 → NAND=1. The logic holds for any number of inputs. Why Other Options Are Wrong: “low” or “0's” would make AND=0, thus NAND=1 (HIGH). “some low and some high” also makes AND=0, so NAND=1. “None of the above” is wrong because “high” is correct. Common Pitfalls: Confusing AND and OR behavior; forgetting that NAND is the exact complement of AND. Final Answer: high

Question 10

Truth table size for a multi-input OR gate: An OR gate has 6 independent inputs. How many distinct input rows (“input words”) appear in its truth table?

Accepted Answer

Introduction / Context: The size of a truth table depends solely on the number of independent binary inputs, not on the specific logic function. Each binary input can be 0 or 1, so the total number of combinations grows exponentially with the number of inputs. Given Data / Assumptions: Gate type: OR (but table size is independent of the function). Number of inputs n = 6. Inputs are binary (0 or 1). Concept / Approach: The number of distinct input combinations is 2^n. This counts every pattern (000000 through 111111 for 6 inputs). Therefore, the OR gate’s truth table has exactly 2^6 rows of input combinations, even though many of those produce the same output value (1) for OR. Step-by-Step Solution: Use formula: count = 2^n.Substitute n = 6 → 2^6 = 64.Thus, 64 input rows appear in the truth table. Verification / Alternative check: Enumerate powers of two: 2^4=16, 2^5=32, 2^6=64, 2^7=128. With six inputs, the correct count is 64, matching 2^6. Why Other Options Are Wrong: 32 and 16 correspond to 5 and 4 inputs respectively; 128 corresponds to 7 inputs. “None of the above” is incorrect because 64 is correct. Common Pitfalls: Confusing output patterns with input rows; the output column content does not change the number of input combinations. Final Answer: 64

Question 11

Combinational logic identification: What function results when an inverter is added to the output of an AND gate?

Accepted Answer

Introduction / Context: Constructing logic from primitive gates is a common exercise. Many complex functions can be reduced to combinations of AND, OR, and NOT (inverter) operations. Recognizing these composites quickly is useful in design and troubleshooting. Given Data / Assumptions: Base function: AND. Modification: add an inverter after the AND output. All logic signals are binary. Concept / Approach: By definition, NAND is the complement of AND. That is, NAND(A,B,...) = NOT(AND(A,B,...)). Therefore, if you take an AND gate and simply invert its output, the resulting function is NAND. This identity generalizes to any number of inputs. Step-by-Step Solution: Start with Y = AND(inputs).Apply inversion: Z = NOT(Y).Thus Z = NOT(AND(inputs)) = NAND(inputs). Verification / Alternative check: Truth tables validate that the output is LOW only when all inputs are HIGH—exactly the behavior of a NAND gate. Why Other Options Are Wrong: NOR = NOT(OR), not NOT(AND). XOR is exclusive OR and cannot be formed by a single inversion of AND. OR is the original OR function, not the complement of AND. Common Pitfalls: Confusing De Morgan’s laws; remember that NOT(AND) is NAND and NOT(OR) is NOR. Final Answer: NAND

Question 12

Mixed-base conversion practice: Convert the octal number 143₈ to its hexadecimal (base 16) equivalent.

Accepted Answer

Introduction / Context: Octal and hexadecimal are compact ways to represent binary values. Converting between them is efficient via an intermediate binary mapping because 8 = 2^3 and 16 = 2^4, so digit boundaries align to groups of 3 or 4 bits respectively. Given Data / Assumptions: Given: 143 in base 8. Find: equivalent in base 16. No change in value; only representation differs. Concept / Approach: Two straightforward methods: (1) convert 143₈ → binary by expanding each octal digit to 3 bits, then regroup into 4-bit nibbles to form hexadecimal; or (2) convert octal → decimal, then decimal → hexadecimal. Both lead to the same result when done correctly. Step-by-Step Solution: Decimal route: 143₈ = 18^2 + 48 + 3 = 64 + 32 + 3 = 99₁₀.Convert 99₁₀ to hex: 99 / 16 = 6 remainder 3 → 0x63.Therefore, 143₈ = 63₁₆. Verification / Alternative check: Binary route: 1→001, 4→100, 3→011; concatenate 001100011₂; group from right into 4-bit nibbles: 0110 0011 → 6 3 → 0x63. Same result confirms correctness. Why Other Options Are Wrong: 6016, 5016, and 5716 correspond to different decimal values. Only 6316 matches 99₁₀ exactly. Common Pitfalls: Mis-grouping bits when switching between 3-bit and 4-bit boundaries; dropping leading zeros that help correct nibble grouping. Final Answer: 6316

Question 13

Memory technologies comparison: The DRAM memory cell is simpler and smaller than the memory cell of which other RAM type?

Accepted Answer

Introduction / Context: Dynamic RAM (DRAM) and Static RAM (SRAM) are two common volatile memory technologies. They trade off density, speed, power, and circuit complexity. Understanding the structural difference between their individual memory cells explains why DRAM achieves higher bit densities. Given Data / Assumptions: DRAM cell uses a capacitor to store charge and a single access transistor. SRAM cell uses a multi-transistor latch (typically 6 transistors in CMOS). Both are volatile (lose data without power), but cell complexity differs. Concept / Approach: Because a DRAM cell is essentially 1T1C (one transistor plus one capacitor), it occupies less silicon area than an SRAM cell, which uses cross-coupled inverters (four transistors) plus two access transistors (total ≈6T). This makes DRAM denser and cheaper per bit, though it requires periodic refresh to maintain charge, whereas SRAM holds state as long as power is applied without refresh. Step-by-Step Solution: Compare DRAM cell (1T1C) to SRAM cell (~6T).Conclude DRAM cell is simpler/smaller; therefore it is smaller than a static RAM cell.Answer: static (SRAM). Verification / Alternative check: Datasheets and textbooks list DRAM as higher density, lower cost per bit, and SRAM as faster and simpler to interface but lower density—directly tied to cell complexity. Why Other Options Are Wrong: “volatile” describes both DRAM and SRAM; it is not a distinct type. “semiconductor” is the broad category for both. “bipolar” refers to a transistor technology, not the standard classification contrasting DRAM vs. SRAM cells here. Common Pitfalls: Confusing system-level simplicity (SRAM interface is simple) with cell-level simplicity (DRAM cell area is smaller). Final Answer: static

Question 14

Decimal to octal conversion exercise: What is the octal (base 8) equivalent of the decimal number 28₁₀?

Accepted Answer

Introduction / Context: Converting decimal to octal is a standard number-system task in digital logic and computer organization. The repeated division method by the target base (here, 8) provides a reliable algorithm for the conversion. Given Data / Assumptions: Given: 28 in base 10. Find: representation in base 8 (octal). Use integer division by 8 and collect remainders. Concept / Approach: Algorithm for decimal → octal: repeatedly divide the decimal number by 8, recording remainders at each step; the octal digits are these remainders read from last to first (bottom to top). This works because of place-value expansion in base 8. Step-by-Step Solution: 28 ÷ 8 = 3 remainder 4 → least significant octal digit is 4.3 ÷ 8 = 0 remainder 3 → next digit is 3; division stops when quotient is 0.Read remainders upward: 3 4 → 34₈. Verification / Alternative check: Check by reconversion: 3*8 + 4 = 24 + 4 = 28, confirming 34₈ is correct. Why Other Options Are Wrong: 32₈ = 26₁₀; 40₈ = 32₁₀; 36₈ = 30₁₀. Only 34₈ equals 28₁₀. Common Pitfalls: Reading remainders in the wrong order; confusing division quotients with remainders. Final Answer: 348

Question 15

Binary width awareness: How many binary bits are required to represent a single hexadecimal digit (0–F)?

Accepted Answer

Introduction / Context: Hexadecimal is a compact notation for binary values. Because hex is base 16 and 16 equals 2^4, there is a direct one-to-one mapping between a single hex digit and four binary bits, known as a nibble. Given Data / Assumptions: Hex digit range: 0–F (0–15 decimal). Binary digits (bits) are base-2. Goal: minimal bit width to encode any hex digit. Concept / Approach: The number of bits required to represent N distinct values is the smallest k such that 2^k ≥ N. For a hex digit, N = 16, and 2^4 = 16, so k = 4. Therefore each hex digit corresponds exactly to a 4-bit binary group, enabling easy grouping and conversion between binary and hex. Step-by-Step Solution: Set N = 16.Find k with 2^k ≥ 16 → k = 4.Therefore, one hex digit requires 4 bits. Verification / Alternative check: Examples: A₁₆ = 10₁₀ = 1010₂ (4 bits), F₁₆ = 15₁₀ = 1111₂ (4 bits). The mapping is exact. Why Other Options Are Wrong: 3 bits hold only 8 values, 6 bits hold 64, and 8 bits hold 256—over/under the needed capacity for a single hex digit. Common Pitfalls: Using 8 bits per hex digit out of habit from byte-aligned thinking; remember, 1 hex digit = 4 bits, 2 hex digits = 8 bits (one byte). Final Answer: 4

Question 16

Octal to decimal conversion practice: Convert the octal number 3137₈ to its decimal (base 10) value.

Accepted Answer

Introduction / Context: Understanding place values in different bases allows you to convert any positional number system into decimal. Octal digits are weighted by powers of 8 from right to left: 8^0, 8^1, 8^2, and so on. Given Data / Assumptions: Given octal: 3 1 3 7 (i.e., 3137₈). Digits are valid octal digits (0–7). Goal: compute the exact decimal value. Concept / Approach: Use the positional expansion: For digits dₙ…d₂ d₁ d₀ in base 8, value = Σ dᵢ * 8^i. Multiply each digit by the corresponding power of 8 and sum the results to obtain the base-10 number. Step-by-Step Solution: Digits and weights: 38^3 + 18^2 + 38^1 + 78^0.Compute powers: 8^3 = 512, 8^2 = 64, 8^1 = 8, 8^0 = 1.Multiply: 3512 = 1536; 164 = 64; 38 = 24; 71 = 7.Sum: 1536 + 64 + 24 + 7 = 1631.Therefore, 3137₈ = 1631₁₀. Verification / Alternative check: Quick sanity: Highest place 3*512 = 1536 already, so the total must be a bit above 1536; 1631 is consistent. Calculator check will match 1631. Why Other Options Are Wrong: 1632₁₀, 1531₁₀, and 1931₁₀ result from digit or power mistakes. Only 1631₁₀ is correct. Common Pitfalls: Reversing digit order; using 10^i instead of 8^i; arithmetic slips when summing partial results. Final Answer: 163110

Question 17

Design intuition in digital vs. analog: Why are digital circuits generally easier to design than analog circuits for most computing tasks?

Accepted Answer

Introduction / Context: Digital design relies on discrete logic levels—typically just two, labeled 0 and 1—rather than continuously varying voltages or currents. This binary abstraction greatly simplifies analysis, noise tolerance, and verification compared with analog circuits, which must maintain precise relationships across a continuum of values. Given Data / Assumptions: Digital circuits use logic thresholds and noise margins. Analog circuits require accurate gain, linearity, bandwidth, and biasing. We are evaluating fundamental design difficulty, not packaging or size alone. Concept / Approach: Because digital logic only needs to discern between distinct ranges (e.g., below V_IL for 0 and above V_IH for 1), many imperfections—component tolerances, small noise spikes—are tolerated within defined noise margins. In contrast, analog systems must maintain precise relationships (e.g., exact voltage levels, continuous-time behavior), making them sensitive to component variation, temperature, and noise. Therefore, digital circuits are generally easier to design functionally for computation tasks. Step-by-Step Solution: Identify the essence: discrete levels vs. continuous control.Relate to noise margins: digital accepts wide variation within level bands.Conclude: digital design effort is reduced compared to analog precision requirements. Verification / Alternative check: Logic families define voltage thresholds and noise margins in datasheets. If your signal stays within these bands, function is correct despite component spread—this is not true for many analog designs. Why Other Options Are Wrong: IC packaging (b) helps both analog and digital; it does not explain “easier design.” (c) Elements may be from one family, but this is not the primary reason. (d) Smaller size can be true but is a consequence, not the fundamental cause. Common Pitfalls: Assuming digital design is always easy; high-speed digital and mixed-signal systems still face analog constraints (signal integrity, timing, EMI). Final Answer: they do not control electricity precisely over a wide range

Question 18

In digital electronics, what do we call a group of storage elements (typically flip-flops) that together store multi-bit digital data as a single unit for immediate processing by a CPU or controller?

Accepted Answer

Introduction / Context: Digital systems rarely work with single bits in isolation. Instead, they bundle several bits into a structure that can be read or written in one operation. This structure is central to arithmetic, instruction execution, address generation, and pipeline staging in processors and controllers. Identifying the correct term clarifies how CPUs move and transform data internally. Given Data / Assumptions: The item stores multiple bits at once. It is composed of storage devices such as flip-flops or latches. It is used inside processors or digital controllers for immediate access. Concept / Approach: A register is a small, fast storage unit made from flip-flops (or latches) that holds a multi-bit word (for example, 8, 16, 32, or 64 bits). Registers sit at the heart of datapaths: accumulator registers, instruction registers, program counters, and general-purpose registers. They differ from memory arrays (RAM/ROM) by offering extremely fast, often single-cycle, access and by being tightly integrated with arithmetic and control logic. Step-by-Step Solution: Identify “group of devices that store digital data” as a multi-bit storage unit.Recall the standard digital term for such a unit: register.Eliminate distractors that do not denote a multi-bit storage unit.Select “register.” Verification / Alternative check: Datapath diagrams show buses connecting ALUs to registers; timing charts show synchronous loading of all bits in a register on a clock edge, which matches the described behavior. Why Other Options Are Wrong: Circuits: too generic; could be any electronic arrangement. Variations: not a technical term for storage. Bit: a single binary digit, not a group. None of the above: incorrect because “register” is precise and standard. Common Pitfalls: Confusing registers with memory locations (RAM cells). A memory location belongs to a large array with address decoding; a register is discrete, CPU-internal, and optimized for speed. Final Answer: register

Question 19

A microprocessor addresses memory locations from 0000 to 3FFF (hex). Each address stores 1 byte. How many bytes are stored in total, and what is this size expressed in kilobytes (KB)?

Accepted Answer

Introduction / Context: Computers commonly express address spaces in hexadecimal because each hex digit corresponds to 4 binary bits. Understanding how to convert a hex address range into a byte count and then into kilobytes is fundamental for memory mapping, stack sizing, and device decoding. Given Data / Assumptions: Address range: 0000 to 3FFF (hex), inclusive. Each address holds exactly 1 byte. 1 KB (K) is taken as 1024 bytes in typical microcomputer contexts. Concept / Approach: The number of addresses from 0000 to 3FFF equals 3FFF − 0000 + 1. In hex, 3FFF + 1 = 4000 (hex). Therefore, the total count is 0x4000 addresses. Convert 0x4000 to decimal: 4 * 16^3 = 4 * 4096 = 16,384 bytes. Expressed in KB: 16,384 / 1024 = 16 KB. Step-by-Step Solution: Compute total addresses: last − first + 1 = 3FFF − 0000 + 1 = 4000 (hex).Convert 4000 (hex) to decimal: 4 * 4096 = 16,384.Relate bytes to kilobytes: 16,384 / 1024 = 16 KB.Select “16,384, 16K.” Verification / Alternative check: Binary view: 0000 to 3FFF spans 14 address bits (since 3FFF = 0011 1111 1111 1111). 2^14 = 16,384 addresses, confirming the same result. Why Other Options Are Wrong: 4,095 (4K) undercounts because it ignores inclusive addressing. 32,740 (32K) and 46,040 (46K) are not powers-of-two aligned with the range. None of the above is wrong because the correct pair exists as option B. Common Pitfalls: Forgetting the +1 when ranges are inclusive; mixing decimal “kilo” (1000) with binary “K” (1024) when expressing memory sizes. Final Answer: 16,384, 16K

Question 20

Convert the hexadecimal number 6B21 (base 16) to its exact binary (base 2) equivalent. Write the full 4-bit nibble for each hex digit.

Accepted Answer

Introduction / Context: Base conversion between hexadecimal and binary is routine in digital design and debugging. Each hex digit maps exactly to a 4-bit binary “nibble,” which makes the conversion quick and error-resistant when done systematically. We will convert 6B21 (hex) into binary by expanding each digit into 4 bits. Given Data / Assumptions: Hex value: 6B21 (base 16). Hex-to-binary mapping uses 4 bits per hex digit. No leading nibbles are omitted; we keep all four nibbles. Concept / Approach: Use the standard mapping: 0→0000, 1→0001, …, 9→1001, A→1010, B→1011, C→1100, D→1111, E→1110, F→1111. Expand each hex digit independently, then concatenate the nibbles in the same order to obtain the full binary string. Step-by-Step Solution: 6 (hex) → 0110.B (hex) → 1011.2 (hex) → 0010.1 (hex) → 0001.Concatenate: 0110 1011 0010 0001 → 0110101100100001. Verification / Alternative check: Convert to decimal quickly: 6B21 (hex) = 616^3 + 1116^2 + 216 + 1 = 64096 + 11*256 + 32 + 1 = 24,576 + 2,816 + 33 = 27,425. Now check the binary value: 0110101100100001 (base 2) equals 27,425 decimal, confirming correctness. Why Other Options Are Wrong: Each distractor differs in at least one nibble, often flipping a bit in the B or 2 positions, which changes the decimal value; only option A preserves the exact nibble mapping of 6, B, 2, 1. Common Pitfalls: Dropping leading zeros in a nibble (e.g., writing 2 as 10 instead of 0010); reversing nibble order; confusing B (1011) with 13 (1101). Always write 4 bits per hex digit. Final Answer: 0110101100100001

Digital Computer Electronics Questions