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64 multiple-choice questions and 21 flashcards on Assembling and Deploying Applications, about 21% of the Databricks Generative AI Engineer bank. Every one carries a written rationale.
Assembling and Deploying Applications is one of 8 chapters in CoStudy's Databricks Generative AI Engineer bank, and it holds 64 of the bank's 300 multiple-choice questions — roughly 21% of the total. That proportion is not arbitrary: chapters follow the certifying body's published exam outline, and the number of questions in each is set by that domain's published weight, so the share of your practice time this chapter takes matches the share of the real exam it accounts for.
Studying by chapter is worth doing once you have a diagnostic score. A single overall percentage tells you whether you are close; it does not tell you which domain is dragging. Working a weak chapter in isolation, and re-testing it in isolation, is the fastest way to move a score that has stalled — and it is why the mock exams in CoStudy report by domain rather than as one number.
2 questions drawn from this chapter, with the full rationale shown — the controlling principle behind the right answer, and why each wrong option tempts and fails.
Provisioned throughput on Model Serving is BEST for:
Answer: B — High, steady traffic with predictable latency
B) Correct — reserved capacity pays off when demand is constant, giving stable latency and a predictable bill. A) Sparse traffic wastes reserved capacity; pay-per-token fits better. C) Reserved capacity is billed whether used or not. D) Serving endpoints do not pre-train models.
Which concern is MOST specific to exposing an agent through a Teams or Slack surface rather than an internal web app?
Answer: C — chat-platform identity must be mapped to a workspace identity for data access
C) Correct — the identity bridge is the crux: without mapping, everyone effectively shares one service principal's data access. A) MLflow logging is required regardless of the front end. B) Indexes are shared, not per-channel. D) Minor formatting differences are not the substantive concern.
4 cards from the 21 in this chapter.
What is Databricks Apps used for in a GenAI solution?
Hosting the interactive front end — a chat UI, review tool or internal web app — inside the workspace, running under governed identity and calling serving endpoints and Unity Catalog data without standing up outside infrastructure.
A/B testing on Databricks Model Serving?
Route % of traffic to candidate model; compare metrics. Promote when candidate wins.
Why should a deployed agent's configuration live in a separate config file rather than being hard-coded?
So prompts, model names, retriever settings and top-k can be changed and versioned without editing agent logic, allowing the same code to be re-logged for different environments and enabling controlled experiments.
Latency budgets?
Plan: tokens-out × per-token latency + retrieval + tool calls. Stream responses to reduce perceived latency.
These are a sample. The full Assembling and Deploying Applications chapter runs 85 items with per-chapter progress tracking, on the web and in the iOS app.
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