Lifelogging can be referred to as the process of passively collecting data on an individual's daily life. Lifelog data provides a large amount of information which can be used to understand the lifelogger's lifestyle and preferences. This data can also support the lifeloggers in saving their memories and important moments. Question-answering (QA) is a common task in natural language processing (NLP) and can be extended to multi-modal such as the visual question-answering task. QA for lifelog data can be described as the task of answering questions about a lifelogger's past using lifelog data, which can significantly help lifeloggers understand their life by asking questions about their lifelog. QA for lifelogs can also provide useful insights into lifelogger's life for those exploring their lifelog. This paper presents the MemoriQA lifelog dataset designed to explore the question-answering task for lifelogs. This dataset provides 61-day lifelog images and other lifelog data such as internet activity, health metrics, music listening history and GPS. A comprehensive annotation process is performed to create the description as well as question-answer pairs. We propose some methods to address the QA in lifelog problem in this paper.