Question 1

A context engineer is debugging a Databricks agent that supports a multi-turn customer support application. The agent keeps a running summary of all conversation history across every user session in a single vector index and retrieves it on every turn through semantic search. Users report that the agent loses track of facts stated earlier in the same session, while unrelated details from other users' past sessions appear unexpectedly. Latency has also increased as conversation volume grows.

Which storage configuration correctly resolves the memory type mismatch that is causing this degraded behavior?

A. Partition the vector index by user ID and session ID so that semantic retrieval is scoped per session, preventing cross-session bleed while reserving long-term facts for all users.
B. Use a session-scoped short-term buffer to retain all within-session conversation turns, and reserve the vector store for durable cross-session user facts retrieved by semantic search.
C. Replace the vector index with a relational table partitioned by session ID, and retrieve all rows matching the current session on every turn to maintain full conversational context for both short-term and long-term memory needs.
D. Use a session-scoped short-term buffer to retain all within-turn context, and write a full conversation summary to the vector index at the end of each turn so semantic retrieval stays current across all active sessions.

Correct Answer: B Explanation:

The application incorrectly combines short-term conversational memory and long-term semantic memory within a single vector retrieval mechanism.

Short-term memory must preserve the sequence of messages, recent user statements, intermediate decisions, and tool interactions within an active session.

Semantic vector retrieval does not guarantee chronological completeness. It retrieves information based on relevance scores, potentially omitting earlier conversation details that remain essential for the current interaction.

Furthermore, storing all users' conversation summaries in a shared index without appropriate access boundaries can cause unrelated information to appear in generated responses.

Option B separates these responsibilities.

A session-scoped short-term buffer maintains the active conversation context, preserving continuity across turns. A separate persistent vector store retains durable information such as confirmed preferences, customer-specific facts, and significant decisions that should remain accessible across sessions.

Databricks provides this architectural distinction through Agent Sessions and Agent Memory, both of which can use Lakebase-backed persistence.

Option A improves retrieval isolation but continues using semantic search as the primary mechanism for session continuity. Option C does not properly distinguish durable memory from session history. Option D continues accumulating redundant summaries and increases retrieval overhead.

Production implementations should also enforce user-scoped access controls, bounded session histories, and relevance-based long-term memory retrieval.

Official Databricks reference: Agent memory and sessions, Agent sessions, and Agent state and memory.

Databricks Context Engineer Associate Dumps By Hernandez

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