Mid-level Software Engineer interview prep questions

An agent loop (also called a ReAct-style or think-act loop) follows a fixed cycle: the agent receives an observation, reasons/thinks about it, selects and calls a tool/action, then receives the next observation. This cycle repeats until a stopping condition is met (e.g., the agent emits a final answer or a max-step limit is reached). 'Train the model' is a one-time offline activity, not part of a runtime agent loop.

True. Standard queues guarantee at-least-once delivery: because SQS stores messages redundantly across servers, a server being briefly unavailable during a delete can cause the same message to be received again. So consumers must be idempotent. If you need duplicates suppressed and strict ordering, you use a FIFO queue, which provides exactly-once processing and ordered delivery (at lower throughput). Assuming a standard queue delivers exactly once is a frequent source of double-charged payments and duplicated side effects in production.

PostgreSQL's query planner chooses between a sequential scan and an index scan based on cost estimates derived from statistics (pg_statistic). When a predicate matches a large fraction of rows (e.g., a low-selectivity condition like status = 'active' on a table where 95% of rows are active), the planner correctly decides that reading the entire heap sequentially is cheaper than random index lookups plus heap fetches. The planner uses the page-level cost model: random I/O has a higher cost constant (random_page_cost) than sequential I/O (seq_page_cost). An index is only beneficial when selectivity is high (few rows returned). Options about outdated statistics (ANALYZE not run) and fillfactor are distractors — the described behavior is correct planner behavior, not a bug.

Webhook reliability requires the consumer to respond quickly (within the provider's timeout window, often 5–30 s) with a 2xx status to acknowledge receipt, then process asynchronously. If the consumer does heavy work synchronously and times out, the provider may retry, causing duplicate deliveries. Idempotency keys/event IDs let consumers deduplicate retries. Signature verification (HMAC) guards against spoofed payloads but does not help with duplicate delivery. Storing the raw payload before processing is the correct 'accept-then-process' pattern. Option C describes returning a 200 immediately and queuing the work — the correct pattern — while the others describe anti-patterns or incomplete solutions.

Python's typing.get_type_hints() resolves forward references and applies __future__ annotations. When from __future__ import annotations is active, ALL annotations are stored as strings (PEP 563 postponed evaluation). get_type_hints() then resolves them at call time using the provided (or inferred) global namespace. Here the forward reference 'Node' is resolved by get_type_hints() to the actual Node class, so hints['next'] returns typing.Optional[Node]. Printing that gives typing.Optional[__main__.Node] (or the equivalent module path). The __annotations__ dict, by contrast, would still hold the raw string.

The Circuit Breaker pattern monitors call failures to a downstream service and, after a threshold is crossed, 'opens' the circuit so that further calls fail immediately rather than waiting for a timeout. This prevents thread pools and other resources in the calling service from being exhausted while the dependency is down. Retries alone worsen the situation by consuming more resources. The Saga and Sidecar patterns address different concerns (distributed transactions and cross-cutting concerns, respectively).

Agent memory architectures are typically decomposed into four distinct tiers that serve different temporal and functional purposes. In-context (working) memory is the active prompt window — cheap to read but bounded by context length. External/episodic memory (e.g., a vector store of past episodes) allows retrieval across unlimited history but requires an explicit lookup step. Semantic/knowledge memory stores structured facts the agent can query. Procedural memory encodes learned skills or tool-use policies, often baked into weights or fine-tuned adapters — NOT the in-context window, which is stateless across sessions. The statement that in-context memory persists across independent agent sessions is false: a fresh invocation starts with an empty context unless explicitly reconstructed from an external store.

The actions are scoped correctly, but Resource: "*" applies them to every object in every bucket in the account — a massive over-grant that violates least privilege. The fix is the bucket's object ARN, arn:aws:s3:::reports-prod/* (and arn:aws:s3:::reports-prod for bucket-level actions if needed). The Version date 2012-10-17 is the current, correct policy-language version — it is not a year you bump. Multiple actions in one statement is normal, and you would never broaden to s3:* to fix an over-permissive policy. Over-broad Resource is one of the most common and dangerous real-world IAM mistakes.

A relation is in BCNF if for every non-trivial functional dependency X → Y, X is a superkey. The given relation R(A, B, C, D) has FDs: AB → C, C → B, and AB → D. Check each: AB → C: AB is a superkey? AB → C and AB → D means AB determines all attributes (AB → ABCD), so yes, AB is a superkey. AB → D: same reasoning, valid. C → B: Is C a superkey? C+ = {C, B} ≠ {A,B,C,D}, so C is NOT a superkey. Therefore C → B violates BCNF. This relation is in 3NF (because B is a prime attribute — it's part of the candidate key AB), but NOT in BCNF. The relation is NOT in 4NF either because it first fails BCNF, and 4NF requires BCNF. The correct answer is 3NF but not BCNF.

Breaking changes are those that can cause existing well-written clients to behave incorrectly without modification. (a) Renaming full_name to name breaks clients that read full_name. (c) Removing internal_score breaks clients that depend on it. (d) Changing id's type from integer to string breaks clients that store or compare it as a number. (e) Returning 200 instead of 404 for a missing resource breaks clients that branch on HTTP status codes to detect absence. Adding a new optional field (b) is generally considered a non-breaking, additive change under most REST versioning contracts.

pytest.mark.skipif skips the test when its condition evaluates to True. The requirement here is to skip when CI is falsy — meaning absent or set to an empty string (e.g., CI=). not os.getenv("CI") is True in both of those cases, making Option B correct. Option A (is None) only returns True when the variable is entirely absent; if CI is exported as an empty string, os.getenv("CI") returns "" (not None), so is None is False and the test would run despite CI being empty — missing the falsy case. Option D inverts the logic and skips when CI is present. Option C unconditionally skips, ignoring the environment variable entirely.

In a multi-region active-active architecture, the core challenge with eventual consistency is handling concurrent writes to the same record from different regions. Vector clocks (or similar mechanisms like CRDTs) track causality across nodes so that conflicts can be detected and resolved rather than silently overwriting data. Last-Write-Wins (LWW) based solely on wall-clock time is unsafe because clocks can skew, making it possible to overwrite a newer write with an older one. Two-phase commit provides strong consistency but introduces cross-region coordination latency and is an availability risk — the opposite of what active-active targets. Read repair is a read-path technique and does not prevent write conflicts. Vector clocks remain the standard causality-tracking answer for active-active replication conflict detection.

LLM-as-a-judge is a popular evaluation technique, but it has well-documented systematic biases. Studies (e.g., from Zheng et al. 2023 on MT-Bench) show that GPT-4 and similar judges exhibit (1) position bias — preferring the first answer shown, (2) verbosity bias — favouring longer answers regardless of correctness, and (3) self-enhancement bias — rating outputs from models of the same family higher. Calibration drift and sycophancy are real but are properties of the target model, not the judge. The key insight for senior engineers is that using a single LLM judge without positional swapping, length normalization, or multi-judge consensus will silently skew evaluation results.

cidr_blocks = ["0.0.0.0/0"] on port 22 exposes SSH to every IP on the internet, inviting brute-force and credential-stuffing attacks — a textbook misconfiguration scanners flag. Restrict it to specific admin/VPN CIDRs, or better, drop public SSH entirely and use AWS Systems Manager Session Manager (no open inbound port at all). The other options are wrong: a single-port rule with equal from_port/to_port is valid, the protocol field takes tcp/udp/etc. (not ssh), and security groups absolutely can use CIDR blocks as well as referencing other groups. Wide-open management ports are among the most common cloud breaches.

ACID's Isolation property is the one that governs how concurrent transactions see each other's in-progress changes. The four standard isolation levels (READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE) are all refinements of the Isolation guarantee. Atomicity ensures all-or-nothing completion; Consistency ensures data remains valid after a transaction; Durability ensures committed changes survive crashes. None of the other three properties concern themselves with concurrency anomalies like dirty reads, non-repeatable reads, or phantoms — those are isolation concerns.