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In this skill badge, you'll learn how MongoDB Vector Search works under the hood and what to do when it slows down in production. You'll build a diagnostic playbook using Atlas Metrics to trace slow queries to their root cause, whether that's memory pressure, an oversized index, or CPU contention. From there, you'll explore the two deployment architectures for MongoDB Vector Search and understand how your choice directly affects how much memory is available to your vector indexes. You'll also apply the two core optimization techniques: quantization, which reduces the memory footprint of your vector index by lowering the precision of each stored dimension, and partial indexing with views, which reduces the number of vectors indexed in the first place. By the end, you'll be equipped to confidently size, monitor, and tune MongoDB Vector Search deployments.
