How to Ace Algorithm Interviews: Insider Tips, Sample Questions, and Evaluation Criteria

Interview Evaluation Framework

Interviewers assess algorithm‑position candidates across five dimensions: fundamentals, depth of knowledge, breadth of knowledge, business understanding, and communication.

Fundamentals

Explain BERT’s architecture: a stack of Transformer encoder layers, token embeddings, positional embeddings, and a special [CLS] token whose final hidden state is used for classification.

Self‑attention formula:

Attention(Q,K,V)=softmax\left(\frac{QK^{T}}{\sqrt{d_k}}\right)V

where Q, K, V are linear projections of the input and d_k is the dimension of the keys.

Pre‑training tasks: Masked Language Modeling (MLM) – randomly mask 15% of tokens and predict them; Next Sentence Prediction (NSP) – predict whether two sentences appear consecutively.

Typical evaluation metrics for a multi‑class text‑classification model: overall accuracy, per‑class precision, recall, F1‑score, and macro‑averaged versions of these.

Metric calculation: e.g., precision = TP/(TP+FP), recall = TP/(TP+FN), F1 = 2·(precision·recall)/(precision+recall). Macro‑averaging computes the metric independently for each class and then averages.

Depth of Knowledge

Pre‑BERT text‑classification methods: TF‑IDF + linear models, bag‑of‑words SVM, CNN/RNN classifiers.

Word2vec drawbacks: static embeddings (no context), out‑of‑vocabulary words, limited to capturing syntactic similarity. BERT provides contextualized embeddings, handles polysemy, and learns from large corpora.

Purpose of the [CLS] token: aggregates sequence‑level information for downstream tasks. Alternatives include mean‑pooling or max‑pooling over token embeddings.

Mask usage in BERT: attention mask to ignore padding tokens; token mask for MLM to hide tokens during pre‑training.

Self‑attention computational complexity: O(L²·d) where L is sequence length and d hidden size. Long texts are handled by truncation, sliding‑window segmentation, or efficient variants such as Longformer, Reformer, or hierarchical attention.

Breadth of Knowledge

Label taxonomy construction: start from domain expert definitions, refine with data‑driven clustering (e.g., hierarchical clustering on embedding space), and iterate with annotator feedback.

Labeling cycle: define a fixed period (e.g., weekly or monthly), accumulate a target volume (e.g., >100k labeled samples), and assess sufficiency via learning‑curve analysis or validation performance plateau.

Improving labeling efficiency: active learning (select uncertain samples), semi‑supervised learning, annotation tools with pre‑filled suggestions.

Handling new categories without full retraining: zero‑shot classification using label embeddings, few‑shot fine‑tuning, or adding a lightweight classifier on top of frozen BERT features.

Long‑tail category mitigation: data augmentation (synonym replacement, back‑translation), transfer learning from related high‑frequency classes, or hierarchical classification to share parameters.

Business Understanding

Deployment scope: quantify the number of product scenes and daily traffic served; conduct A/B tests comparing the model‑enabled pipeline against a baseline.

Business‑level metrics beyond accuracy: reduction in manual review volume, time‑to‑decision, conversion rate, or audit‑efficiency gain.

Quantifying value: e.g., a 10% increase in automated classification accuracy translates to X hours saved per day, measured against upstream data ingestion and downstream content moderation pipelines.

Communication & Expression

Interviewers look for structured, logical articulation. Recommended frameworks include:

Chronological: “first, second, third”.

Context‑action‑result: “background, actions, results”.

Answers should be concise, demonstrate active listening, and avoid interrupting the interviewer.

Practical Coding Questions

Three difficulty levels assess algorithmic thinking and implementation skill.

Easy

Given an array of positive integers, find the minimum absolute difference between any two elements.

def min_abs_diff(arr):
    arr.sort()
    return min(abs(arr[i] - arr[i-1]) for i in range(1, len(arr)))

Medium

Given two arrays of positive integers, pick one element from each array to minimize the absolute difference.

def min_abs_diff_two_arrays(a, b):
    a.sort()
    b.sort()
    i = j = 0
    best = float('inf')
    while i < len(a) and j < len(b):
        best = min(best, abs(a[i] - b[j]))
        if a[i] < b[j]:
            i += 1
        else:
            j += 1
    return best

Hard

Given N arrays of positive integers, select one element from each array ( x₁ … x_N) to minimize the sum of consecutive absolute differences Σ|x_i - x_{i+1}|.

import heapq

def min_chain_diff(arrays):
    # Dynamic programming: dp[i][v] = minimal cost ending with value v from arrays[i]
    dp = {v: 0 for v in arrays[0]}
    for arr in arrays[1:]:
        new_dp = {}
        sorted_prev = sorted(dp.items(), key=lambda x: x[0])
        # Pre‑compute prefix minima for efficiency
        prefix_min = []
        cur_min = float('inf')
        for val, cost in sorted_prev:
            cur_min = min(cur_min, cost - val)
            prefix_min.append((val, cur_min))
        suffix_min = []
        cur_min = float('inf')
        for val, cost in reversed(sorted_prev):
            cur_min = min(cur_min, cost + val)
            suffix_min.append((val, cur_min))
        suffix_min.reverse()
        for v in arr:
            # binary search position in sorted_prev
            import bisect
            pos = bisect.bisect_left([p[0] for p in sorted_prev], v)
            best = float('inf')
            if pos > 0:
                best = min(best, v + prefix_min[pos-1][1])
            if pos < len(sorted_prev):
                best = min(best, -v + suffix_min[pos][1])
            new_dp[v] = best
        dp = new_dp
    return min(dp.values())

The hard version requires dynamic programming with efficient state transition (e.g., using sorted previous values and prefix/suffix minima) to keep the complexity manageable.