AWS CERTIFIED AI PRACTITIONER
EXAMINATION SET 2026 SOLVED
QUESTIONS GRADED A+
● General AI. Answer: A theoretical form of AI with broad problem-
solving abilities, much like a human (different from generative AI)
● Machine Learning (ML). Answer: ML involves using algorithms and
statistical models to allow computers to perform tasks by learning data
rather than following explicit instructions (subset of AI)
● ML Models. Answer: ML models improve over time as more data is
provided to them (finds patterns/predictions in training data)
● Deep Learning. Answer: a subset of ML that uses multi-layered
neural networks to model and solve more complex problems
● Neural Networks. Answer: Input layer > (translated through math)
hidden layers > output layer
● Comparing AI, ML and Deep Learning. Answer: AI: program that can
sense reason, act and adapt
ML: algorithm whose performance improve as they are exposed to more
data over time
, DL: subset of ML in which multi-layered neural networks learn from
vast amount of data
● AI, ML & DL Differences. Answer: AI: General intelligence
simulations
ML: Learning from data patterns
DL: Complex pattern recognition with neural networks
● When to use ML. Answer: Ideal for simpler tasks requiring predictive
analysis
● When to use DL. Answer: Suited for complex, large scale tasks
requiring high precision
● Inferencing. Answer: the process in which AI models make
predictions or decisions using new data
● Real-time Inferencing. Answer: processes the data instantly as it
arrives (chatbots, fraud detection, autonomous driving systems (ex.
Amazon Sagemaker)
● Batch Inferencing. Answer: processes data in bulk at scheduled
intervals (sentiment analysis on social media posts collected over a day)
EXAMINATION SET 2026 SOLVED
QUESTIONS GRADED A+
● General AI. Answer: A theoretical form of AI with broad problem-
solving abilities, much like a human (different from generative AI)
● Machine Learning (ML). Answer: ML involves using algorithms and
statistical models to allow computers to perform tasks by learning data
rather than following explicit instructions (subset of AI)
● ML Models. Answer: ML models improve over time as more data is
provided to them (finds patterns/predictions in training data)
● Deep Learning. Answer: a subset of ML that uses multi-layered
neural networks to model and solve more complex problems
● Neural Networks. Answer: Input layer > (translated through math)
hidden layers > output layer
● Comparing AI, ML and Deep Learning. Answer: AI: program that can
sense reason, act and adapt
ML: algorithm whose performance improve as they are exposed to more
data over time
, DL: subset of ML in which multi-layered neural networks learn from
vast amount of data
● AI, ML & DL Differences. Answer: AI: General intelligence
simulations
ML: Learning from data patterns
DL: Complex pattern recognition with neural networks
● When to use ML. Answer: Ideal for simpler tasks requiring predictive
analysis
● When to use DL. Answer: Suited for complex, large scale tasks
requiring high precision
● Inferencing. Answer: the process in which AI models make
predictions or decisions using new data
● Real-time Inferencing. Answer: processes the data instantly as it
arrives (chatbots, fraud detection, autonomous driving systems (ex.
Amazon Sagemaker)
● Batch Inferencing. Answer: processes data in bulk at scheduled
intervals (sentiment analysis on social media posts collected over a day)
