Competency-based education (CBE) in computing aims to equip learners with both technical and non-technical competencies (knowledge, skills, and dispositions), which are crucial for success in the rapidly evolving field of computing. The ability to effectively assess these competencies is essential, but there is a gap in understanding how different methods are utilized, particularly in computing education. This work-in-progress study presents a systematic review of competency assessment methods used in computing education, focusing on how these methods assess both technical competencies, such as programming and algorithm design, as well as non-technical competencies, including teamwork, communication, and problem-solving. The review also explores emerging trends in assessment, such as AI-driven assessments and gamified approaches, that aim to address current limitations and enhance learner engagement. Preliminary findings reveal that traditional assessment methods, such as written exams and project-based assessments, are still widely adopted to measure technical skills. However, these approaches are often inadequate in assessing non-technical competencies. Innovative assessment methods, like AI-driven tools and gamification, are gaining traction as potential alternatives, providing adaptive and personalized experiences that could help bridge this gap. The review aims to highlight the strengths, weaknesses, and future directions for assessment practices in competency-based computing education. It ultimately seeks to contribute to a deeper understanding of how assessment strategies can be improved to ensure students acquire the competencies necessary for professional success in the field, aligning learning outcomes with the evolving needs of the computing industry.