CFDE
Free GSDC Certified Forward Deployed Engineer Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$4006 exam domainsLevel Intermediate2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A Forward Deployed Engineer (FDE) is embedded on-site with a logistics client. The VP of Operations says, "Our dispatch process is chaotic and costing us money — fix it with AI." There is no defined dataset, no clear metric, and three different teams describe the problem differently. What should the FDE do FIRST?
The FDE mindset requires structuring an ambiguous problem before reaching for technology. When three teams describe the same problem differently, the correct first move is problem decomposition and reconciling conflicting inputs (a MECE-style breakdown) to find the real, well-defined problem before any modeling or tooling decision is made.
A team is building a FastAPI microservice where every route handler needs a validated database session that must be properly closed after the response is sent, and the team also needs to be able to swap in a mock session during unit tests. Which approach BEST meets these requirements?
A generator-based dependency with `yield`, injected via `Depends()`, gives FastAPI clean setup/teardown semantics — code before `yield` runs on request start, code after runs after the response — and FastAPI's dependency_overrides mechanism lets tests substitute a fake session without touching route code.
A retail company ingests raw clickstream data into a cloud data warehouse with virtually unlimited elastic compute and storage (e.g., Snowflake). They want to preserve the raw, unaltered data for future reprocessing needs and let analysts transform it using SQL directly inside the warehouse. Which approach BEST fits this requirement?
Modern cloud warehouses with elastic, cheap compute favor ELT: load raw data first, then transform in-place with SQL. This preserves the raw source for reprocessing and offloads transformation to scalable warehouse compute — the exact scenario described.
A team builds a RAG system over 500-page legal contracts. Users ask about a payment-terms clause but the system returns text about an unrelated warranty section. The pipeline uses fixed 512-token chunks with zero overlap and retrieves the top-3 chunks by cosine similarity. Which change is MOST likely to fix this?
Fixed-size chunking with no overlap frequently splits a clause across two chunks or mixes unrelated clauses together, destroying the semantic unit an embedding needs to represent accurately. Structure-aware chunking (splitting on clause/section boundaries) plus overlap keeps each clause coherent, so its embedding is a faithful representation and retrieval precision improves.
A client needs an automated system to process incoming invoices: extract vendor name, amount, and date, validate against a fixed set of business rules, and route to one of three approval queues. The steps never change and must run in under 200ms with 100% predictable behavior. Which approach BEST fits this requirement?
When steps are fixed, well-defined, and require low-latency deterministic behavior, a straightforward pipeline of function calls is faster, cheaper, and more predictable than an agent loop — this is the core 'when NOT to use agents' judgment call the exam tests.
A mid-size retailer's VP of Operations requests a live demo of an AI inventory forecasting tool for the executive team next week. During prep, the FDE discovers the model's accuracy has degraded due to recent data drift. What is the BEST next step?
Transparency paired with a concrete mitigation plan preserves trust while still respecting the executive's time investment — this is the core of crisis management protocol for FDEs.
Which statement most accurately distinguishes the Forward Deployed Engineer (FDE) role from a traditional Solutions Architect role, per the GSDC CFDE framing?
The CFDE framing positions the FDE as a hybrid engineer-consultant embedded with the client, doing both business-to-technical translation and hands-on implementation/iteration, unlike a Solutions Architect who typically focuses on high-level architecture and design and hands off build work to other teams.
In production, Service A makes a synchronous REST call to Service B on every incoming request. When Service B experiences high latency, Service A's worker threads block waiting on the response, and the entire system becomes unresponsive. Which change BEST improves the resilience of this inter-service communication?
A circuit breaker with an enforced timeout fails fast when Service B is unhealthy, prevents Service A's threads from blocking indefinitely, and lets the system degrade gracefully with a fallback response instead of cascading into total unavailability.
A bank must detect fraudulent transactions and block them within 200 milliseconds of occurrence, before the transaction settles. Which pipeline pattern should the FDE recommend?
A sub-second latency requirement (200ms) can only be met by a true streaming architecture with an event broker and real-time stream processors that act on each event as it arrives.
A team is moving a RAG prototype into production. Production requirements are: horizontal scalability to tens of millions of vectors, built-in metadata filtering on queries, multi-region replication, and no infrastructure operated by the team itself. Which option BEST satisfies these requirements?
Pinecone is a fully managed, cloud-hosted vector database offering automatic horizontal scaling, native metadata filtering, and multi-region replication without the team managing servers -- exactly matching the stated production requirements.
In a multi-agent system, a 'researcher' agent and a 'critic' agent keep passing the same task back and forth, each rejecting the other's output without converging, and the process never terminates. What is the BEST first fix?
Unbounded agent-to-agent loops are a classic multi-agent reliability failure; the standard guardrail is a hard iteration/turn cap plus a supervisor or convergence check that forces termination.
During a live demo, an executive asks a pointed question about data privacy compliance that the FDE cannot fully answer on the spot. What is the BEST response?
Post-demo follow-up protocol requires honest acknowledgment of unknowns plus a committed, tracked action item — this builds credibility rather than eroding it.
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How much does the CFDE exam cost?
The exam fee is approximately $400 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 6 domains: FDE Mindset and Problem Structuring (~10%), Production Python, Backend Development, and API Design (~18%), Data Engineering, ML Foundations, and Deep Learning Essentials (~20%), Prompt Engineering, LLM Workflows, and RAG (~20%), Agentic Systems, Rapid Prototyping, and Cloud Deployment (~18%), Client Engagement, Consulting, and Demo Delivery (~14%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $34.99 (list $109.99) through September 14 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.