Lyft interview questions

It adds the same constant to every logit, a monotonic shift, so the order is unchanged and ranking metrics like AUC stay identical. At a fixed threshold the boundary moves: a positive shift labels more examples positive, so recall rises or holds and precision usually falls. Calibration breaks, since predicted probabilities no longer match observed rates. It's equivalent to moving the threshold, which is how prior correction after resampling works.

Permissions first: store each chunk with its source's access-control list and filter by the user's identity inside the retrieval call, so the model never sees what the user can't open. Sync ACL changes fast; stale permissions are a leak.

The agent plans searches, reformulates and opens documents, but every answer must cite passages, and a checker verifies claims against them. With no relevant evidence, or conflicting evidence, it abstains and suggests owners. Evaluate recall, citation accuracy, correct abstention, and permission tests with users of different access.

A transformer is an architecture. An embedding model is usually a transformer encoder trained to turn a text into one vector, so similar texts sit close; a generative one predicts the next token. Pretraining learns general language from unlabeled text; fine-tuning adapts those weights to a task with a smaller, targeted dataset.

def kmeans(points, init, max_iter=100, tol=1e-9):
    cents = [list(c) for c in init]
    for _ in range(max_iter):
        groups = [[] for _ in cents]
        for p in points:
            d = [sum((a - b) ** 2 for a, b in zip(p, c))
                 for c in cents]
            groups[d.index(min(d))].append(p)
        new = []
        for c, g in zip(cents, groups):
            if not g:
                new.append(c)
                continue
            n = len(g)
            new.append([sum(x) / n for x in zip(*g)])
        shift = max(sum((a - b) ** 2
                        for a, b in zip(c, m))
                    for c, m in zip(cents, new))
        cents = new
        if shift <= tol:
            break
    return cents, groups

State each rule before coding. Given initial centroids make runs deterministic. A tie goes to the lowest-index centroid, which taking the first minimum does. An empty cluster keeps its previous centroid; reseeding at the farthest point is the alternative. Stop when no centroid moves more than a tolerance, or at the iteration cap. Interviewers weigh these edge cases more than the loop.