Regular mammography screening is essential for early breast cancer detection. Deep learning-based risk prediction methods have sparked interest to adjust screening intervals for high-risk groups. While early methods focused only on current mammograms, recent approaches leverage the temporal aspect of screenings to track breast tissue changes overtime, requiring spatial alignment across different time points. Two main strategies for this have emerged: explicit feature alignment through deformable registration and implicit learned alignment using techniques like transformers, with the former providing more control. However, the optimal approach for explicit alignment in mammography remains underexplored. In this study, we provide insights into where explicit alignment should occur (input space vs. representation space) and if alignmentand risk prediction should be jointly optimized. We demonstrate thatjointly learning explicit alignment in representation space while optimizing risk estimation performance, as done in the current state-of-the-artapproach, results in a trade-off between alignment quality and predictive performance and show that image-level alignment is superior torepresentation-level alignment, leading to better deformation field qualityand enhanced risk prediction accuracy. The code is available at https://github.com/sot176/Longitudinal_Mammogram_Alignment.git