Agentic Systems, Rapid Prototyping, and Cloud Deployment
Free GSDC Certified Forward Deployed Engineer practice — 6 questions on Agentic Systems, Rapid Prototyping, and Cloud Deployment, with explanations. No sign-up. Full 12-question mixed test →
A client asks you to automate their invoice intake process: extract fields from PDF invoices, validate the total against a purchase order in their ERP, and route the invoice to the correct approver based on a fixed amount threshold. The steps never branch based on judgment calls, and no external research or dynamic tool selection is needed. Which approach BEST fits this requirement?
The workflow is fully deterministic with no dynamic decision-making or tool selection required at runtime. A scripted pipeline with a single LLM extraction step is faster, cheaper, more reliable, and easier to debug than an autonomous agent loop — this is a core FDE judgment call: agents add value only when the path isn't known in advance.
In a production multi-agent system, a researcher agent and a writer agent are designed to collaborate: the writer requests more research whenever it judges the draft insufficient. In testing, the two agents occasionally loop for over 40 turns without producing a final answer, consuming excessive tokens. Which fix BEST addresses this reliability issue?
Unbounded agent-to-agent loops are a known reliability failure mode in multi-agent systems. The standard guardrail is a hard iteration cap with a supervising controller that halts execution and escalates rather than letting the loop run indefinitely — this preserves the collaborative design while bounding cost and risk.
A customer support agent must recall the resolution of a specific ticket a customer references three weeks later, in a completely new session with no shared conversation history. Which memory architecture is required to support this?
Recalling specific past interactions across sessions requires persistent, retrievable episodic memory — storing past interactions with metadata (customer ID, timestamp) in a vector store and retrieving them via semantic search is the standard pattern for this requirement.
Your team is containerizing a Python AI application whose build process requires compiling several packages with native dependencies, but the runtime only needs the compiled artifacts. Which Dockerfile strategy produces the smallest, most secure production image?
Multi-stage builds are the standard pattern for keeping production images minimal: compilers and build tools stay in the discarded builder stage, and only the resulting artifacts (e.g., a populated virtualenv or wheel files) are copied into the slim runtime image, reducing both size and attack surface.
A client's chatbot backend calls an LLM API for every incoming user question. Analytics show thousands of near-duplicate questions phrased differently by different users each day, driving up API costs. Which caching strategy is MOST effective at reducing redundant LLM calls without degrading response relevance?
Semantic caching compares embedding similarity rather than exact strings, so paraphrased or reworded questions with the same intent still hit the cache — directly solving the near-duplicate problem described, while a similarity threshold protects against serving irrelevant cached answers.
You need to build a client-facing prototype with multiple distinct pages (dashboard, chat interface, analytics view), a persistent sidebar for navigation between them, and token-by-token streaming of LLM responses in the chat page. Which approach BEST fits these requirements?
Streamlit's native multipage app support (`pages/` directory) combined with sidebar navigation is purpose-built for distinct, navigable views, and `st.write_stream` handles token-by-token streaming cleanly — matching all three stated requirements in one framework.
Ready for the real thing?
The full course: two full-length practice tests, video lessons for every exam domain, hands-on labs and detailed explanations.
$109.99 $34.99 with code FREETEST33 — valid through September 14.
Get my $34.99 deal →