1. Apple's Photographic Styles system (iOS 15, 2021) addressed the scene-recognition transparency problem in what way?
✓ Correct. Photographic Styles lets users set a preferred tone and contrast rendering that the AI applies consistently — the user's choice persists rather than being overridden by per-scene AI decisions.
✗ Photographic Styles made aesthetic preferences explicit and persistent: users define their preferred rendering, and the AI maintains it across scene types instead of silently applying its own per-scene defaults.
2. The US Copyright Office's 2023 Zarya of the Dawn ruling established what principle relevant to style-transferred photographs?
✓ Correct. The ruling distinguished between AI-generated content (not registrable) and human creative decisions made within an AI-assisted workflow (potentially protectable) — a distinction directly relevant to photographers who make deliberate stylization choices.
✗ Incorrect. The Zarya ruling established that purely AI-generated portions are not registrable, but that human creative choices — selection, arrangement, retouching — within an AI-assisted workflow may still be protectable expression.
3. What is the EU AI Act's specific data transparency requirement for providers of general-purpose AI models regarding training data?
✓ Correct — ✓ Correct. The EU AI Act requires general-purpose AI model providers to publish training data summaries detailed enough for copyright holders to identify whether their work was used and pursue claims.
✗ Incorrect. The EU AI Act requires "sufficiently detailed summaries" of training data — the first legislative mandate for AI training data transparency — specifically to enable copyright holders to assert rights.
4. What does Imagen AI primarily help photographers with?
✓ Correct. Imagen AI automates the culling and rating process, significantly reducing the time photographers spend selecting keepers from large shoots.
✗ Imagen AI is an AI culling and rating tool — it automatically selects the best images from a shoot, reducing post-production overhead for high-volume photographers.
5. The March 2023 Pentagon explosion hoax involved an AI-generated image that caused measurable real-world harm. What was that harm?
✓ Correct — ✓ Correct. The image spread before verification could catch up, demonstrating that AI-generated disinformation can cause measurable economic harm even when debunked quickly.
✗ Incorrect. The hoax briefly triggered a dip in U.S. stock markets — a concrete demonstration that AI-generated visual disinformation can produce real financial consequences before corrections circulate.
6. Which classical interpolation method samples a 4×4 neighborhood of 16 pixels using a weighted curve to estimate new pixel values?
✓ Bicubic interpolation uses 16 surrounding pixels (4×4 grid) and a smooth cubic weighting function, producing softer results with better edge retention than bilinear's 4-pixel average.
✗ Bicubic interpolation is the 16-pixel, cubic-weighted method. Nearest-neighbor duplicates one pixel; bilinear averages four; Lanczos uses a sinc-based kernel over a larger neighborhood.
7. Which organisation co-founded the Content Authenticity Initiative (CAI) in 2019, which developed the C2PA standard?
✓ Correct — ✓ Correct. Adobe, BBC, and The New York Times co-founded the CAI in 2019, later developing the C2PA technical standard for content provenance and authenticity.
✗ Incorrect. The CAI was co-founded by Adobe, the BBC, and The New York Times in 2019.
8. Which characteristic makes impressionist and post-impressionist oil paintings particularly effective as style transfer references compared to most other art forms?
✓ Correct. Impressionist brushwork — short strokes creating fine detail, medium-scale pattern, and large-scale tonal structure — is an ideal match for the multi-scale texture statistics that Gram matrices capture and that style transfer propagates.
✗ Incorrect. The key factor is multi-scale texture: impressionist brushwork creates rich texture at fine, medium, and coarse scales simultaneously — exactly what Gram matrices at multiple CNN layers are designed to encode and transfer.
9. The "Gender Shades" study (Buolamwini & Gebru, 2018) measured which specific problem in commercial AI systems?
✓ Correct — ✓ Correct. Gender Shades quantified intersectional demographic bias in facial analysis accuracy — a landmark study that prompted IBM, Microsoft, and Amazon to audit their products.
✗ Incorrect. Gender Shades specifically measured facial analysis error rates across demographic groups, finding up to 34.7% errors for darker-skinned women vs. 0.8% for lighter-skinned men.
10. Which camera manufacturer was first to implement C2PA cryptographic signing directly in camera firmware, announced in 2024?
✓ Correct. Nikon and Leica were the first manufacturers to embed C2PA signing in camera firmware, enabling provenance authentication at the moment of capture.
✗ Nikon (Z9 and subsequent models) and Leica were the first manufacturers to implement in-camera C2PA signing in 2024.
11. In the generative fill workflow, which three inputs does a latent diffusion inpainting model receive simultaneously?
✓ Correct. The inpainting model receives: the full image encoded into latent space, a binary mask indicating which regions to regenerate, and an optional text prompt for semantic guidance. These three inputs together determine the fill output.
✗ Inpainting models take three primary inputs: the full image in latent space, a binary mask (1 = fill, 0 = preserve), and an optional text prompt. The text prompt provides semantic steering while the masked latents and surrounding context provide structural conditioning.
12. Google Brain's Vincent Dumoulin introduced what technique in 2017 that allowed a single network to handle multiple styles without retraining?
✓ Correct. Conditional instance normalization stores separate normalization parameters (scale and shift) for each style; selecting a style simply swaps these parameters in the network, enabling dozens of styles in a single model.
✗ Incorrect. The technique was conditional instance normalization — storing separate learned normalization parameters per style, so switching styles means only swapping a small set of parameters within one network.
13. What is the primary distinction between CNN-based style transfer and diffusion-based img2img style transfer that makes CNN approaches sometimes preferable in professional workflows?
✓ Correct. Repeatability and precise reference matching are the CNN approach's key advantages. For photographers who need the same stylization applied consistently across a series, or who need exactly the look of a specific reference, CNN methods remain preferable.
✗ Incorrect. The key professional advantage of CNN style transfer is determinism and precise reference matching — identical inputs produce identical outputs, and the output specifically reflects the Gram matrix statistics of the chosen reference image.
14. SRCNN, the first end-to-end deep learning super-resolution model, was published in which year?
✓ Correct. SRCNN was published in 2014 by Chao Dong et al. at CUHK, marking the beginning of deep learning approaches to single-image super-resolution.
✗ SRCNN (Super-Resolution Convolutional Neural Network) was published in 2014 by Chao Dong and colleagues at the Chinese University of Hong Kong — the first end-to-end trained deep learning approach to SR.
15. Which lawsuit filed in January 2023 signaled to enterprise clients that licensed training data would become a commercial necessity for AI image tools?
✓ Correct — ✓ Correct. Getty Images filed a lawsuit against Stability AI in January 2023, alleging that Stability had scraped 12 million Getty images without consent. This signaled to enterprise clients that provenance guarantees were essential.
✗ Incorrect. Getty Images sued Stability AI in January 2023, alleging unauthorized use of 12 million Getty images for training. This case drove enterprise clients toward Adobe Firefly's licensed-data model.
16. What is the primary reason AI culling tools cannot perfectly score sharpness on raw files?
✓ Correct. AI culling tools analyze JPEG previews or compressed proxies embedded in the raw file — never the full bit-depth raw sensor data — which can cause sharpness scoring inaccuracies.
✗ Incorrect. The key limitation is that AI culling tools work from JPEG previews or compressed proxies, not the actual raw sensor data, affecting sharpness measurement accuracy.
17. ControlNet (Zhang & Agrawala, 2023) solves what specific problem in diffusion-based style transfer for photographers?
✓ Correct. ControlNet adds structural conditioning inputs — edge maps, depth maps, segmentation — that constrain the diffusion process to preserve compositional structure regardless of denoising strength.
✗ Incorrect. ControlNet addresses the compositional preservation problem: it adds structural conditioning (edge maps, depth maps) that preserve the photograph's layout even at high denoising strengths.
18. What happened to Google Photos' solution to its 2015 gorilla-labelling bias problem, as reported by Wired in 2023?
✓ Correct — ✓ Correct. Rather than fixing the training data, Google suppressed the relevant search terms — a technical patch that avoided the underlying representational problem for years.
✗ Incorrect. Google's reported solution was to block searches for primate-related terms entirely — a content filter rather than a fix — persisting for at least eight years.
19. What standard does Adobe use to embed machine-readable provenance metadata into images processed with Firefly generative fill?
✓ Correct. Adobe embeds Content Credentials following the C2PA (Coalition for Content Provenance and Authenticity) standard. This includes a cryptographically signed manifest indicating AI content was added, enabling downstream verification.
✗ Adobe uses the C2PA (Coalition for Content Provenance and Authenticity) standard, branded as Content Credentials, to embed provenance metadata into Firefly-processed images. This creates a cryptographically verifiable record of AI involvement.
20. A contrastive ranking model for aesthetic scoring trains on what type of data?
✓ Correct. Contrastive ranking models learn from pairwise preferences — which of two images is preferred — making them theoretically more flexible than absolute-label regression models.
✗ Incorrect. Contrastive ranking models train on pairs of images with human-indicated preferences, learning relative quality rather than absolute scores.