📝 Peer to peer architecture introduction (9 MCQs)

📖 From Data Communication and Networks • 29. Peer to Peer Paradigm • 9 questions available

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What is Peer to peer architecture introduction?

Peer-to-peer architecture is a decentralized network model where every computer, known as a peer, acts as both a client and a server to share resources directly with others without relying on a central authority.

Easy

Medium

Hard

📝 All Peer to peer architecture introduction MCQs

Q1. Which researchers introduced arithmetic coding in 1981?

A.Rissanen and Langdon ✅

B.Shannon and Huffman

C.Lempel and Ziv

D.Baker and Miller

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: The technique was first described by Jorma Rissanen and Thomas Langdon in 1981. Their work laid the foundation for arithmetic coding, distinguishing it from earlier methods such as Huffman coding. Recognizing the original authors is a factual recall that anchors the historical context of the method.

Q2. When the probability of a symbol increases, how does its sub‑interval change within the [low,high) range?

A.It becomes smaller

B.It becomes larger ✅

C.It stays the same

D.It becomes negative

💡 Difficulty: easy | ✅ Correct: B

📖 Explanation: A higher probability allocates a larger portion of the total interval to that symbol, because the sub‑interval length is proportional to the symbol’s probability. Thus, increasing the probability expands the sub‑interval, making it larger within the overall coding range.

Q3. Which statement best contrasts arithmetic coding with Huffman coding regarding handling of symbol probabilities?

A.Arithmetic coding uses fixed‑length codes

B.Huffman coding can represent fractional bits

C.Arithmetic coding can represent any probability distribution without integer‑bit constraints ✅

D.Huffman coding always yields shorter codes

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: Arithmetic coding maps the whole message to a real‑valued interval, allowing it to exploit arbitrary probability values and produce fractional‑bit efficiencies. Huffman coding, by contrast, assigns integer‑length codewords based on a binary tree, limiting its ability to match non‑power‑of‑two probabilities exactly.

Q4. Given a binary alphabet {0,1} with $P(0)=0.7$ and $P(1)=0.3$ , what is the shortest binary representation of the interval after encoding the sequence '01'?

A.0.01

B.0.1 ✅

C.0.11

D.0.101

💡 Difficulty: medium | ✅ Correct: B

📖 Explanation: First, encoding '0' yields the interval [0,0.7). Then encoding '1' narrows it to [0.49,0.7) because the cumulative probability before '1' is 0.7 and the interval length after the first symbol is 0.7. The binary fraction 0.1 (which equals 0.5) lies inside this interval and is the shortest binary string that uniquely identifies it.

Q5. During decoding, which logical condition ensures the decoder stops at the correct point?

A.When the interval width equals zero

B.When the binary pattern length exceeds the interval width

C.When the interval width is less than

2^{-k}

where

k

is the number of bits read ✅

D.When the high value becomes 1

💡 Difficulty: medium | ✅ Correct: C

📖 Explanation: The decoder reads bits until the current interval becomes narrower than the resolution provided by the bits read. If the interval width is smaller than $2^{-k}$ , any additional bits would not change the interval’s containment of the pattern, indicating that the message has been uniquely identified.

Q6. How does an error in estimated symbol probabilities affect the compression efficiency of arithmetic coding compared to Huffman coding?

A.Arithmetic coding is unaffected

B.Huffman coding suffers more

C.Both degrade equally

D.Arithmetic coding's efficiency drops more because interval misalignment leads to longer codes ✅

💡 Difficulty: hard | ✅ Correct: D

📖 Explanation: Arithmetic coding relies on precise probability estimates to partition the interval accurately. Misestimated probabilities cause the interval to be allocated incorrectly, which can increase the number of bits needed to represent the message. Huffman coding, using integer‑length codewords, is less sensitive to small probability errors, so arithmetic coding typically suffers a greater efficiency loss.

Q7. Why does arithmetic coding keep the interval as a half‑open interval $[low,high)$ rather than a closed interval?

A.To avoid ambiguity when the pattern equals the high bound ✅

B.Because closed intervals are easier to compute

C.To allow overlapping intervals

D.No specific reason

💡 Difficulty: medium | ✅ Correct: A

📖 Explanation: Using a half‑open interval guarantees that each possible binary pattern maps to a unique sub‑interval. If the interval were closed at both ends, a pattern that exactly equals the high bound could belong to two adjacent intervals, causing decoding ambiguity. The half‑open convention eliminates this overlap.

Q8. What happens to the length of the interval after each additional symbol is encoded?

A.It decreases ✅

B.It increases

C.It stays the same

D.It becomes zero

💡 Difficulty: easy | ✅ Correct: A

📖 Explanation: Each symbol narrows the interval because the current interval is subdivided according to the symbol’s probability. Multiplying the interval length by a probability less than one always yields a smaller length, so the interval continuously shrinks as more symbols are processed.

Q9. If the final interval after encoding a message is $[0.8125,0.875)$ , which binary string is the shortest valid representation of the message?

A.0.1101 ✅

B.0.111

C.0.1001

D.0.11

💡 Difficulty: hard | ✅ Correct: A

📖 Explanation: The lower bound 0.8125 equals the binary fraction 0.1101. Any binary string that falls within the interval must be at least as large as this bound but less than 0.875 (0.111). The shortest such string is the exact lower bound itself, 0.1101, which uniquely identifies the interval without exceeding the upper limit.

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