Chapter 1 Quiz: The Landscape of AI Engineering
Test your understanding of the key concepts from Chapter 1. Each question has a single best answer. Try to answer each question before revealing the solution.
Question 1. The 1956 Dartmouth workshop is significant in the history of AI because it:
a) Produced the first working AI program b) Formally established AI as a field of study c) Demonstrated the first neural network d) Solved the knowledge acquisition bottleneck
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**b) Formally established AI as a field of study.** The Dartmouth workshop, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, is widely regarded as the founding event of artificial intelligence as a formal academic discipline. While some AI programs existed before the workshop, it was at Dartmouth that the field was named and its research agenda was articulated.Question 2. Which of the following best describes the "knowledge acquisition bottleneck" that limited symbolic AI?
a) Computers were too slow to process symbolic representations b) There was not enough training data available for symbolic systems c) Manually encoding the vast, implicit knowledge humans use for reasoning proved extremely difficult d) Symbolic AI could not be implemented in programming languages of the era
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**c) Manually encoding the vast, implicit knowledge humans use for reasoning proved extremely difficult.** The knowledge acquisition bottleneck was the fundamental challenge of expert systems and symbolic AI: while domain-specific knowledge could be encoded as rules, capturing the breadth and nuance of common-sense reasoning was prohibitively difficult and labor-intensive.Question 3. The backpropagation algorithm, popularized in 1986, enabled:
a) Symbolic AI systems to learn from data b) Training of multi-layer neural networks by computing gradients c) GPUs to be used for neural network training d) The creation of the first expert systems
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**b) Training of multi-layer neural networks by computing gradients.** Backpropagation provided a practical method for computing the gradient of a loss function with respect to the weights of a multi-layer neural network, enabling these networks to be trained using gradient descent. This was a crucial advance that made deep neural networks feasible as a learning method.Question 4. AlexNet's victory in the 2012 ImageNet challenge was historically significant because:
a) It was the first neural network to beat a human at image recognition b) It demonstrated that deep convolutional networks could dramatically outperform traditional computer vision methods c) It introduced the transformer architecture d) It proved that unsupervised learning was superior to supervised learning
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**b) It demonstrated that deep convolutional networks could dramatically outperform traditional computer vision methods.** AlexNet reduced the top-5 error rate from 26.2% to 15.3% on ImageNet, a dramatic improvement over the best hand-engineered feature pipeline approaches. This result convinced much of the computer vision community to adopt deep learning and catalyzed the deep learning revolution across AI.Question 5. The three factors that converged to enable the deep learning revolution were:
a) Symbolic AI, machine learning, and neural networks b) Python, GPUs, and cloud computing c) Data, compute, and algorithms d) Research funding, industry interest, and open-source software
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**c) Data, compute, and algorithms.** The deep learning revolution was enabled by the convergence of large datasets (from the internet), powerful computational hardware (GPUs), and algorithmic advances (deeper architectures, better activation functions, improved optimization methods). All three ingredients were necessary --- the algorithms existed earlier but could not be realized without sufficient data and compute.Question 6. The transformer architecture, introduced in 2017, is based primarily on which mechanism?
a) Recurrent connections b) Convolutional filters c) Self-attention d) Reinforcement learning
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**c) Self-attention.** The transformer architecture, introduced in "Attention Is All You Need" (Vaswani et al., 2017), replaced recurrent and convolutional building blocks with self-attention mechanisms that can process entire sequences in parallel. This enabled much more efficient training on long sequences and became the foundation for models like BERT and GPT.Question 7. A "foundation model" is best described as:
a) The first model trained for any new AI project b) A large model pre-trained on broad data that can be adapted to many downstream tasks c) A model that uses only fundamental mathematical operations d) The baseline model used for performance comparison
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**b) A large model pre-trained on broad data that can be adapted to many downstream tasks.** Foundation models represent a paradigm shift where a single large model is pre-trained on diverse data and then adapted (via fine-tuning, prompting, or other techniques) to a wide variety of specific tasks. Examples include GPT-4, BERT, and DALL-E.Question 8. Which learning paradigm involves an agent interacting with an environment and receiving rewards or penalties?
a) Supervised learning b) Unsupervised learning c) Reinforcement learning d) Transfer learning
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**c) Reinforcement learning.** In reinforcement learning, an agent learns to make sequences of decisions by interacting with an environment. It receives rewards (or penalties) based on its actions and adjusts its strategy to maximize cumulative reward over time. This is distinct from supervised learning (which uses labeled examples) and unsupervised learning (which finds structure in unlabeled data).Question 9. In the modern AI stack, which layer is responsible for tracking model performance and detecting data drift after deployment?
a) Hardware layer b) Training and experimentation layer c) Serving and deployment layer d) Monitoring and observability layer
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**d) Monitoring and observability layer.** The monitoring and observability layer uses tools like Evidently AI, Arize, and WhyLabs to continuously track model performance, detect data drift, and alert when prediction quality degrades. This is critical for maintaining AI systems in production over time.Question 10. NumPy is described as foundational to the Python AI ecosystem because:
a) It is the only library that can perform matrix multiplication b) Its n-dimensional arrays and vectorized operations form the bedrock for nearly all other AI/ML libraries c) It includes built-in deep learning functionality d) It was the first Python package ever created
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**b) Its n-dimensional arrays and vectorized operations form the bedrock for nearly all other AI/ML libraries.** NumPy provides the fundamental data structure (the ndarray) and numerical operations that virtually all other Python AI/ML libraries are built upon. Libraries like scikit-learn, PyTorch, and TensorFlow all interact with or build on top of NumPy's array interface.Question 11. The primary difference between an AI engineer and a data scientist is:
a) AI engineers use Python while data scientists use R b) AI engineers focus on building production systems while data scientists focus on extracting insights to inform decisions c) AI engineers work only with deep learning while data scientists work only with statistics d) AI engineers work in industry while data scientists work in academia
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**b) AI engineers focus on building production systems while data scientists focus on extracting insights to inform decisions.** The key distinction is between automation (building intelligent systems) and insight (analyzing data to inform decisions). A data scientist might discover that certain customer behaviors predict churn, while an AI engineer would build a production system that automatically identifies and acts on those patterns.Question 12. The "Hidden Technical Debt in Machine Learning Systems" paper (Sculley et al., 2015) highlights that:
a) ML models always accumulate technical debt faster than traditional software b) The ML model code typically represents only a small fraction of the total system c) Technical debt in ML systems can be eliminated through proper testing d) Only large companies experience technical debt in ML systems
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**b) The ML model code typically represents only a small fraction of the total system.** The paper demonstrates that the actual ML model code is typically a small percentage (roughly 5--10%) of the total effort in a production ML system. The majority of effort goes into data collection, feature extraction, serving infrastructure, monitoring, configuration, and process management.Question 13. Which of the following is NOT one of the three primary machine learning paradigms?
a) Supervised learning b) Transfer learning c) Unsupervised learning d) Reinforcement learning
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**b) Transfer learning.** The three primary machine learning paradigms are supervised learning, unsupervised learning, and reinforcement learning. Transfer learning is an important technique (applying knowledge from one task to another) but is not considered one of the three fundamental paradigms.Question 14. An AI engineer observes that their model performs well on the test set but poorly in production. Which of the following is the most likely explanation?
a) The test set is too small b) The production data distribution differs from the training/test data distribution c) The model has too many parameters d) The inference hardware is different from the training hardware
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**b) The production data distribution differs from the training/test data distribution.** This is a classic case of distribution shift (also called dataset shift or domain shift). When the data encountered in production differs systematically from the data used for training and evaluation, model performance degrades. This is one of the key challenges that distinguishes AI engineering from pure ML research.Question 15. The "T-shaped skill profile" for AI engineers refers to:
a) Deep expertise in exactly two areas b) Broad knowledge across the AI stack combined with deep expertise in one or two areas c) A career trajectory that changes direction midway d) The shape of a typical learning curve for AI skills
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**b) Broad knowledge across the AI stack combined with deep expertise in one or two areas.** The T-shaped profile metaphor describes professionals who have broad, general knowledge across many areas (the horizontal bar of the T) while maintaining deep expertise in one or two specific areas (the vertical bar). This is considered ideal for AI engineers who must work across the full stack but need specialized depth to make expert contributions.Question 16. Which of the following historical AI systems was an expert system for medical diagnosis?
a) SHRDLU b) AlexNet c) MYCIN d) AlphaGo
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**c) MYCIN.** MYCIN (1976) was an expert system developed at Stanford University that used if-then rules to diagnose bacterial infections and recommend antibiotics. It achieved accuracy comparable to human infectious disease specialists and is one of the most well-known examples of the symbolic AI / expert system approach.Question 17. In the context of AI systems, "non-determinism" means:
a) The system cannot be implemented on deterministic hardware b) The system may produce different outputs for the same input or its behavior cannot be fully specified in advance c) The system uses random number generators exclusively d) The system is unreliable and should not be deployed
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**b) The system may produce different outputs for the same input or its behavior cannot be fully specified in advance.** Non-determinism in AI systems arises because model outputs are probabilistic in nature and depend on learned parameters rather than explicit rules. This is a fundamental difference from traditional software engineering and creates unique challenges for testing, debugging, and deployment.Question 18. A company has 10,000 labeled images and wants to build an image classifier. The most pragmatic approach, according to the chapter, would be to:
a) Immediately train the largest available transformer model b) Start with a simple baseline, measure performance, and add complexity incrementally c) Collect 10 million more labeled images before starting d) Use a rule-based symbolic approach
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**b) Start with a simple baseline, measure performance, and add complexity incrementally.** The chapter emphasizes pragmatism and iteration as hallmarks of effective AI engineering. The recommended approach is to start with the simplest method that might work, establish a baseline, and then add complexity only when justified by evidence. For 10,000 images, fine-tuning a pre-trained model would likely be a good early step after establishing a simpler baseline.Question 19. Which of the following best describes the role of an MLOps engineer?
a) Designing novel machine learning algorithms b) Managing the operational aspects of ML systems including CI/CD, monitoring, and reliability c) Collecting and labeling training data d) Building user interfaces for AI products
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**b) Managing the operational aspects of ML systems including CI/CD, monitoring, and reliability.** MLOps engineers specialize in the operational infrastructure of ML systems, analogous to how DevOps engineers manage software operations. Their responsibilities include continuous integration/continuous deployment for models, monitoring model performance, automating retraining pipelines, and ensuring system reliability.Question 20. The formula $\hat{y} = \mathbf{w}^T \mathbf{x} + b$ represents:
a) A deep neural network b) A linear regression model c) A transformer self-attention layer d) A convolutional filter
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**b) A linear regression model.** This is the equation for a linear model, where $\hat{y}$ is the prediction, $\mathbf{w}$ is the weight vector, $\mathbf{x}$ is the input feature vector, $b$ is the bias term, and $\mathbf{w}^T \mathbf{x}$ is the dot product of the weights and inputs. This simple model is fundamental in machine learning and illustrates the interconnection of linear algebra, calculus, and statistics.Question 21. Generative Adversarial Networks (GANs), introduced in 2014, work by:
a) Generating images from text descriptions using attention mechanisms b) Training two networks --- a generator and a discriminator --- in competition c) Compressing images into a latent space and then reconstructing them d) Learning to reverse a gradual noising process
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**b) Training two networks --- a generator and a discriminator --- in competition.** GANs work by training a generator network (which produces synthetic data) and a discriminator network (which distinguishes real from synthetic data) simultaneously. The generator improves by trying to fool the discriminator, while the discriminator improves by better detecting fakes. This adversarial training process produces increasingly realistic generated outputs.Question 22. The concept of "AI winters" refers to:
a) Periods when AI research is conducted exclusively in cold climate regions b) Seasonal variation in AI research funding c) Periods of reduced funding and interest following unmet expectations d) The time required to cool GPUs during intensive training
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**c) Periods of reduced funding and interest following unmet expectations.** AI winters are historical periods when public and institutional enthusiasm for AI waned dramatically, leading to significant reductions in research funding and commercial investment. These downturns followed periods of overly optimistic predictions that failed to materialize, most notably in the mid-1970s and late 1980s.Question 23. A feature store in the modern AI stack is used for:
a) Storing the source code of feature extraction functions b) Providing a centralized repository of precomputed, reusable features for model training and serving c) Shopping for pre-built AI models d) Storing physical hardware components
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**b) Providing a centralized repository of precomputed, reusable features for model training and serving.** A feature store is a data management system designed specifically for machine learning features. It stores precomputed feature values, ensures consistency between training and serving (avoiding training-serving skew), enables feature sharing across teams and models, and provides point-in-time feature lookups.Question 24. Which of the following ethical considerations involves ensuring that an AI model performs equitably across different demographic groups?
a) Transparency b) Privacy c) Fairness d) Accountability
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**c) Fairness.** Fairness in AI refers to ensuring that models do not systematically disadvantage particular demographic groups. This includes examining whether the model's accuracy, error rates, and outcomes are equitable across groups defined by attributes such as race, gender, age, and socioeconomic status. Achieving fairness requires careful attention to training data, model design, and evaluation metrics.Question 25. According to the chapter, the most effective approach for an AI engineer facing a new problem is to:
a) Immediately implement the most recent state-of-the-art model from a research paper b) Spend several months researching the problem before writing any code c) Start with a simple baseline, measure rigorously, and iterate with increasing complexity d) Survey all possible approaches and implement the theoretically optimal one