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Answer (Detailed Solution Below)

Option 2 : Fragment-aware carving such as bifragment gap carving, which tests reassembly hypotheses across gaps

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Detailed Solution

सही उत्तर Fragment-aware carving such as bifragment gap carving, which tests reassembly hypotheses across gaps है।

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PrepSnapprepsnap.in • Part -A

Question:

A deleted JPEG is suspected to be fragmented across non-contiguous clusters. Which carving approach can still recover it? / संदेह है कि डिलीट JPEG गैर-सतत क्लस्टरों में खंडित है। कौन-सी कार्विंग विधि फिर भी इसे पुनर्प्राप्त कर सकती है?

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Simple header-footer carving, which assumes the file occupies contiguous clusters
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Fragment-aware carving such as bifragment gap carving, which tests reassembly hypotheses across gaps
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Keyword string search, which reconstructs image pixel data from text fragments
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MFT resident-attribute parsing, which works on any filesystem

Correct Answer: Fragment-aware carving such as bifragment gap carving, which tests reassembly hypotheses across gaps

Header-footer carving fails when fragments are scattered; bifragment/smart carving statistically reassembles fragments across gaps, which is the only listed method designed for fragmentation. / हेडर-फुटर कार्विंग खंडित फाइलों पर विफल होती है; बाइफ्रैगमेंट कार्विंग अंतरालों में टुकड़ों को पुनः जोड़ती है। Option (a) is the trap — it only works for contiguous files.

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