Exam Introduction-to-Cryptography Overview - Introduction-to-Cryptography: WGU Introduction to Cryptography HNO1 First-grade Exam Overview

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WGU Introduction-to-Cryptography Exam Syllabus Topics:

SectionObjectives
Key Management and PKI- Key exchange and lifecycle management
- Certificates, certificate authorities, and PKI structure
Cryptographic Protocols and Applications- TLS/SSL conceptual overview
- Secure communication design principles
Asymmetric Encryption- Public key cryptography principles
- RSA and ECC fundamentals
Foundations of Cryptography- Core concepts of confidentiality, integrity, authentication, non-repudiation
- Historical and modern cryptography principles
Symmetric Encryption- AES and legacy algorithms (e.g., DES conceptually)
- Block and stream ciphers
Hash Functions and Message Authentication- MAC and HMAC mechanisms
- Cryptographic hash functions (e.g., SHA family concepts)

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WGU Introduction to Cryptography HNO1 Sample Questions (Q10-Q15):

NEW QUESTION # 10
(Which number generator has different results given the same input data?)

Answer: B

Explanation:
A true random number generator (TRNG) produces outputs derived from nondeterministic physical processes (e.g., thermal noise, oscillator jitter, radioactive decay, or other hardware entropy sources).
Because the underlying phenomenon is not algorithmically determined by an input seed in the same way as a PRNG, repeated "inputs" (or identical conditions from a software perspective) do not yield the same sequence; the outputs vary unpredictably. By contrast, a pseudorandom number generator (PRNG) is deterministic: given the same seed and internal state, it produces the same output sequence, which is useful for repeatability but means security depends on seed secrecy and proper seeding.
"Prime" is not a generator type, and "sequence" is too generic and does not imply nondeterminism. In cryptographic systems, TRNGs (or hardware entropy sources) are often used to seed cryptographically secure PRNGs (CSPRNGs), combining high-quality entropy with efficient generation. Therefore, the generator that can produce different results for the "same input data" is a true random number generator.


NEW QUESTION # 11
(Why did the National Institute of Standards and Technology (NIST) choose Ascon for lightweight cryptography?)

Answer: B

Explanation:
NIST's lightweight cryptography effort targets environments like IoT and embedded systems where CPU, memory, energy, and bandwidth are constrained, yet strong security is still required. Ascon is an authenticated encryption with associated data (AEAD) family designed to be efficient in both hardware and software with small footprint, making it well-suited for constrained devices. NIST selected Ascon because it offers a strong security design with good performance and implementability under tight resource budgets, while providing modern protections (confidentiality + integrity) through AEAD. That aligns with option C: secure and efficient encryption for resource-constrained devices. The selection was not primarily about authenticating users (that is typically handled by protocols and identity systems, not an AEAD primitive). It was also not mainly about legacy compatibility; lightweight cryptography aims at new and constrained deployments rather than preserving outdated stacks. And while Ascon can certainly be used to protect data at rest, that is only one application; the core reason for the choice is its suitability for constrained environments and robust, efficient authenticated encryption.


NEW QUESTION # 12
(What is the value of 51 mod 11?)

Answer: C

Explanation:
The value 51 mod 11 is the remainder after dividing 51 by 11. Modular arithmetic is widely used in cryptography to keep computations within a finite set of residues, such as in RSA where values are taken modulo n, or in Diffie-Hellman where exponents and group elements are reduced modulo a prime. To compute 51 mod 11, find the largest multiple of 11 less than or equal to 51. Multiples of 11 are 11, 22, 33, 44,
55. The closest without exceeding 51 is 44. Subtracting gives 51 # 44 = 7, so the remainder is 7. Therefore, 51 mod 11 = 7, matching option "07." This remainder is always in the range 0 through 10 because the modulus is
11. Such residue computations underpin the "wraparound" behavior that makes modular exponentiation and inverse computations well-defined in cryptographic groups.


NEW QUESTION # 13
(Which authentication method allows a customer to authenticate to a web service?)

Answer: C

Explanation:
One-way client authentication is the method where the client (customer) proves its identity to the server (web service). In cryptographic terms, this is commonly implemented through client credentials such as client TLS certificates (mTLS from the server's perspective) or through authentication protocols layered over TLS (for example, signed tokens), but the defining direction is that the client is the party being authenticated. In a strict TLS certificate-authentication framing, client authentication occurs when the server requests a client certificate during the handshake and the client demonstrates possession of the corresponding private key (via signature in handshake messages). The server then validates the client certificate chain and authorization policy. One-way server authentication, by contrast, authenticates only the server to the client and does not identify the customer. Mutual authentication authenticates both sides simultaneously; while it includes client authentication, it is broader than what the question asks. "End-to-end authentication" describes assurance between endpoints across intermediaries, but it is not the specific "customer authenticates to service" method in certificate-based terminology. Therefore, the best answer is one-way client authentication.


NEW QUESTION # 14
(What describes a true random number generator?)

Answer: D

Explanation:
A true random number generator (TRNG) draws randomness from physical phenomena that are inherently unpredictable and not algorithmically reproducible. Because of this, it is nondeterministic: you cannot feed it the same "input" and expect the same output stream. TRNGs are often slower than PRNGs because they depend on collecting entropy from hardware sources and may require conditioning to remove bias. This aligns with option B: slow and nondeterministic, producing different results even under similar or repeated conditions. Option A describes a deterministic PRNG, where identical seeds yield identical sequences. Option C is unrelated; factorization is a hard math problem used in cryptography (e.g., RSA security assumptions), not a randomness generator definition. Option D describes a counter, which is deterministic and not random.
In secure systems, TRNG output may seed a cryptographically secure PRNG to provide both unpredictability and high throughput; but the defining characteristic of a TRNG is nondeterminism from physical entropy.
Therefore, option B is correct.


NEW QUESTION # 15
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