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This research developed a Kencorpus Swahili Question Answering Dataset KenSwQuAD from raw data of Swahili language, which is a low resource language predominantly spoken in Eastern African and also has speakers in other parts of the world. Question Answering datasets are important for machine comprehension of natural language processing tasks such as internet search and dialog systems. However, before such machine learning systems can perform these tasks, they need training data such as the gold standard Question Answering (QA) set developed in this research. The research engaged annotators to formulate question answer pairs from Swahili texts that had been collected by the Kencorpus project, a Kenyan languages corpus that collected data from three Kenyan languages. The total Swahili data collection had 2,585 texts, out of which we annotated 1,445 story texts with at least 5 QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the annotated texts was subjected to re-evaluation by different annotators who confirmed that the QA pairs were all correctly annotated. A proof of concept on applying the set to machine learning on the question answering task confirmed that the dataset can be used for such practical tasks. The research therefore developed KenSwQuAD, a question-answer dataset for Swahili that is useful to the natural language processing community who need training and gold standard sets for their machine learning applications. The research also contributed to the resourcing of the Swahili language which is important for communication around the globe. Updating this set and providing similar sets for other low resource languages is an important research area that is worthy of further research. Acknowledgement of annotators: Rose Felynix Nyaboke, Alice Gachachi Muchemi, Patrick Ndung'u, Eric Omundi Magutu, Henry Masinde, Naomi Muthoni Gitau, Mark Bwire Erusmo, Victor Orembe Wandera, Frankline Owino, Geoffrey Sagwe Ombui (2022-02-01)