ALT LDS worked trace of an AI agent with max_steps=3. Each of three model decisions requests one tool, which runs. The exhausted loop returns needs_review without a fourth model decision. Pseudocode shows a bounded loop and an early return for a final answer. A note says step caps need separate tool timeouts. Official LDS logo, Let's Data Science and letsdatascience.com appear below.

ALT LDS worked example for LLM label evaluation. Expected label: approved. Ignore case and outer spaces; extra words fail. APPROVED with outer spaces passes after normalization. not approved and approved later fail exact comparison, but both contain approved. Code strips outer spaces, lowercases, then compares the whole string. Official LDS logo, Let's Data Science and letsdatascience.com appear below.

ALT LDS decision map for LLM memory planning. Exactly 8 billion four-bit weights occupy 4 decimal GB before overhead. Check loaded weights, then room for KV cache and runtime buffers. If either fails, adjust model or workload and retest. If both pass, measure task quality and latency. Official LDS logo, Let's Data Science and letsdatascience.com appear below.

ALT SQL contributor challenge with illustrative input. Posts: user 7 published twice, 9 hidden, 12 published. Comments: 7 published, 9 published, 15 hidden, 18 published. Return user_id for anyone with a published post OR comment, once per user, sorted ascending. The answer is withheld. Official LDS logo, Let's Data Science and letsdatascience.com appear below.

ALT Pandas slice example with sorted unique index labels 10, 20, 30, 40, positions 0, 1, 2, 3, and values A, B, C, D. df.loc[10:30] returns A, B, C because its stop label is included. df.iloc[0:2] returns A, B because its stop position is excluded. Official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT Python recall challenge with an illustrative Python 3 example: a = [[1], [2]]; b = a.copy(); b[0].append(9); b.append([3]). Predict a and b before running the code, and explain which objects each append changes. Blank answer boxes withhold the solution. The footer shows the official LDS logo, Let's Data Science and letsdatascience.com.

ALT Illustrative ML feature timeline: value 2 at 09:55 is eligible for a 10:00 prediction; value 7 at 10:05 is future data. Assume immediate availability. Data Engineer portfolio evidence is an as-of join test excluding the future row. ML Engineer evidence is re-evaluation on corrected features. Responsibilities overlap. Official LDS logo, Let's Data Science and letsdatascience.com appear below.

ALT LLM prompt experiment assignment check. For an illustrative test with one prompt variant per user, user 17 receives A, B, A across three turns in the broken trace and A, A, A in the stable trace. Audit user ID, experiment ID and prompt version. This checks assignment only and does not identify a winning prompt. Official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT Illustrative fine-tuning data check: held-out name Rex, primary name Milo, story 'Milo helped REX cross the river.' The primary-name check misses the leak; the whole-row check finds REX and rejects the row. Scan request, story and primary name using whole-word matching that ignores case, before writing splits. Official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT AI portfolio test, labeled conceptual illustration with a mock ticket tool. Approved action: close_ticket(ticket_id=42). Proposed execution: close_ticket(ticket_id=99). The target changed. Expected trace: argument mismatch, block tool call, request new approval. Assert that close_ticket was not called. Keep the approved action, attempted action and tool-call log. Official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT LoRA architecture for one illustrative linear layer. Input x splits into frozen W0, 4096 by 4096, and trainable A, 16 by 4096, then B, 4096 by 16, scaled by alpha/r. The two outputs add to y. A and B contain 131,072 trainable parameters versus 16,777,216 in the full matrix, 128 times fewer for this layer. These counts do not measure total GPU memory or speed. Official LDS logo, Let's Data Science and letsdatascience.com appear below.

ALT Analytics engineering comparison using illustrative data. Orders (order_id, customer_id) are (101,7) and (102,99); customers are 7 and 8. Unique order IDs pass because 101 and 102 differ. The customer relationship fails because ID 99 is missing from customers. A passing uniqueness test does not prove a valid relationship. Portfolio task: keep this failing case with your tests. The official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT Two-panel comic explaining k-nearest-neighbors classification. The closest example has label A, followed by two B neighbors. With uniform weights and no distance ties, k=1 predicts A; k=3 predicts B by two votes to one. The lesson is to check k and the voting rule before explaining a prediction. This is an illustrative example, not an accuracy comparison. Official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT Illustrative embedding search example: query (1,0), candidate A (3,4), candidate B (2,0). Raw dot scores are 3 for A and 2 for B, ranking A first. Cosine scores are 0.60 for A and 1.00 for B, ranking B first. Unit normalization gives A=(0.6,0.8) and B=(1,0), so dot product equals cosine. Check the model's recommended metric and normalization. Official LDS logo, Let's Data Science and letsdatascience.com appear in the footer.

ALT Next-token training worked example with illustrative token IDs [17, 42, 9, 31]. Input tokens[:-1] is [17, 42, 9]; target tokens[1:] is [42, 9, 31]. Arrows connect each input position to its next-token target. At input position 2, the visible prefix [17, 42] predicts 9. Positions are 1-based, with no start or end tokens added. Check the first and last pair before training. Official LDS logo, Let's Data Science and letsdatascience.com appear below.