Certified Context Engineer Associate Exam - Free Databricks Questions and Answers

Question #11 (Topic: Exam A)

A context engineer maintains a Genie space for a finance team. Users repeatedly ask the same shape of question with different fiscal periods: “What is the total open pipeline by region for FY26 Q1?”, “...for FY26 Q2?” “...for the last quarter?”. The SQL pattern Genie generates is identical across these questions (same JOIN, GROUP BY, and aggregation, with only the fiscal-period filter changing), and the team requires that responses to these recurring questions be labeled Trusted in the chat UI. The engineer must register an asset that reuses the same vetted SQL pattern with the user-supplied fiscal period.
Which curation choice should the engineer make?

A. A parameterized example SQL query with a typed (Date or String) fiscal-period parameter, so Genie can reuse the SQL pattern with the user-supplied value each time the question is asked. B. A Unity Catalog SQL function that defines open_pipeline as a reusable measure, with Genie composing the surrounding per-period and per-region SQL around the function call each turn. C. A general instructions text block that describes the desired SELECT, JOIN, WHERE, and GROUP BY structure so Genie can regenerate the SQL on every turn from that description. D. Multiple static example SQL queries (one for each commonly asked fiscal period: FY26 Q1, FY26 Q2, last quarter), so Genie matches each user question to its corresponding vetted example.

Answer: A

Question #12 (Topic: Exam A)

A context engineer is building a RAG pipeline for a pharmaceutical knowledge base. Document chunks from clinical trial summaries are stored in a Unity Catalog-governed Delta table with precomputed embedding vectors. Pure vector similarity search retrieves topically related chunks well but misses chunks that turn on precise drug compound names and dosage identifiers in the search string, because those tokens are underrepresented in the embedding space. The engineer wants to recover those exact-term chunks while preserving the semantic retrieval that already works, without standing up a separate keyword index or adding a downstream merge step.
Which configuration should the engineer apply to close this retrieval gap?

A. Query the index using a filter with a LIKE operator in conjunction with approximate nearest neighbors (ANN) to return a single ranking while recovering the specific exact-term chunks being ignored in the original search. B. Query the index using full-text search, then leverage the Databricks reranker with approximate nearest neighbors (ANN) to return a single hybrid ranking. C. Query the index with hybrid search, using approximate nearest neighbors (ANN) and full-text search in one call and returning a single fused ranking. D. Query the index using approximate nearest neighbors (ANN), then filter the returned chunks to those containing the exact drug and dosage terms, resulting in a single ranked result set.

Answer: C

Question #13 (Topic: Exam A)

A context engineer is building an agent that supports legal teams reviewing multi-part pharmaceutical trial protocols. Each protocol document contains a narrative background section, nested dosage tables with footnote qualifiers, cross-referenced section identifiers, and figure captions describing study design diagrams. The agent must answer precise questions such as which protocol section governs a specific dosage threshold and what footnote conditions apply to that threshold. Available data sources include: a PDF binary store in Unity Catalog Volumes; a Delta table of plain-text chunks extracted from the protocols, each tagged with page number and document identifier, indexed in a Databricks AI Search index; and a Delta table of protocol structure metadata at the page level, containing table summaries, figure captions, and section hierarchy, accessible through Unity Catalog functions.
Which context elements should the context engineer include to answer structural and footnote-level questions accurately?

A. The structure metadata Delta table loaded entirely into the prompt as a hierarchy, combined with the Databricks AI Search index, so the model can resolve table and footnote references from the full metadata. B. The PDF binary store in Unity Catalog Volumes and the structure metadata Delta table, using document identifiers to locate source files and extract table content through a preprocessing step at query time. C. The Databricks AI Search index over plain-text chunks alone, relying on semantic similarity to surface table and footnote content without consulting the structure metadata Delta table. D. The Databricks AI Search index over plain-text chunks to retrieve relevant passages, then a scoped lookup into the structure metadata Delta table for table summaries and figure context on the matched pages.

Answer: D

Question #14 (Topic: Exam A)

A context engineer is reviewing a Databricks agent pipeline built for a contract review assistant. Users submit queries like “Can I get out of this?” and “What does this cover?”. The pipeline retrieves syntactically valid contract clauses via a vector search index, yet generated responses consistently address the wrong contractual context. Tracing logs confirm the vector search index, prompt assembly, and LLM generation stages all execute without errors. The pipeline stages are: input ingestion, pre-retrieval enrichment, context retrieval, prompt construction, and response synthesis.
At which pipeline stage should the context engineer intervene to ensure retrieval operates on a grounded representation of the user’s contractual request?

A. The pre-retrieval enrichment stage, where vague natural-language queries are rewritten into contract-domain representations so that the vector search index embeds a grounded query signal. B. The context retrieval stage, where a multi-route dispatcher selects among domain-partitioned vector indexes based on query embedding similarity scores. C. The response synthesis stage, where the LLM is instructed via system-prompt constraints to restrict output to the contractual domain most relevant to the user’s session history. D. The prompt construction stage, where retrieved contract clauses are inserted into a structured template and formatted before being passed to the LLM.

Answer: A

Question #15 (Topic: Exam A)

A context engineer is investigating why a Genie space serving the sales operations team produces inconsistent answers to revenue questions. When business users ask the same question across different sessions, for example, “What was Q3 EMEA revenue?”, Genie returns different figures. The space is configured over a Unity Catalog managed table of transaction-level data.
Reviewing the space, the engineer finds:
The underlying table has comprehensive table and column comments.
The space’s text instructions describe how revenue should be computed, including currency conversion against a daily FX-rate table, accrual-timing rules, and intercompany-elimination logic.
The space includes several sample questions covering quarterly and regional revenue patterns.
In the query history, Genie composes a new SQL statement for the revenue calculation in each session, and the FX-conversion and elimination steps differ from run to run.
Which change will most reliably stabilize the space’s answers to this team’s revenue questions?

A. Encapsulate the canonical revenue calculation in a parameterized Unity Catalog SQL function and register it in the space as a trusted asset. B. Add a complete example SQL query that performs the full revenue calculation to the space so Genie can learn the pattern. C. Expand the text instructions with step-by-step pseudocode for the revenue calculation, giving Genie a more explicit procedure to follow when generating SQL. D. Define a SQL expression for revenue in the space’s knowledge store so Genie has a reusable definition of the metric.

Answer: A

Databricks Certified Context Engineer Associate - Certified Context Engineer Associate Exam