Assembling and Deploying Applications

DB-GENAI · 44 questions

• The customs-compliance agent runs on a Foundation Model APIs model with a steady, high volume of requests during business hours, and the team already determined provisioned throughput is the right purchasing mode. When configuring the Model Serving endpoint, which setting most directly reflects that decision?
• After logging the customs-compliance agent with MLflow, the team wants to register it in Unity Catalog so that access, lineage and governance apply the same way they do to its source tables. Which identifier correctly reflects Unity Catalog's registration model?
• Meridian Line wants to serve a third-party foundation model that is not hosted on Databricks, using it as one option the charter-desk assistant can call, while keeping the API key out of application code and keeping usage visible through the same interface used for Databricks-native models. Which Model Serving capability is designed for exactly this?
• An engineer finishes testing the charter-desk RAG chain in a notebook and wants to log it with MLflow so it can later be registered and served. Which practice ensures the logged model can be deployed reliably to a Model Serving endpoint without manual patching?
• The maintenance-triage agent's Model Serving endpoint gets heavy use during two shift-change windows each day and sits nearly idle the rest of the time. The team wants to avoid paying for reserved capacity during the long idle stretches while still supporting the busy windows automatically. Which endpoint setting fits this pattern best?
• Before Meridian Line can create a Delta Sync index over its portcirculars table, an engineer needs to stand up the compute that will serve vector search queries. Which object provides that compute, separate from the index itself?
• A new hire asks for the correct end-to-end order of steps to take the safety-review summarizer from a working notebook to a live, queryable application. Which sequence is correct?
• As part of deploying the port-operations copilot, an engineer is creating the Vector Search index over the portcirculars Delta table, which is updated by a nightly batch job and does not need sub-minute freshness. Which sync mode should the engineer choose for the Delta Sync index?
• Meridian Line's platform team wants to cap the customs-compliance endpoint at a maximum number of queries per minute per calling application, so a misbehaving integration cannot overwhelm the shared endpoint or run up cost unexpectedly. Which AI Gateway capability directly provides this?
• A compliance auditor asks Meridian Line to produce a record of every prompt sent to and every response returned by the customs-compliance agent's serving endpoint over the last quarter, for regulatory review. Which AI Gateway capability, enabled ahead of time, would make this possible?
• The charter-desk assistant has a new registered model version that passed evaluation and the team wants production traffic to start hitting it without changing any application code that references the endpoint's model reference. Which mechanism supports this?
• Before fully promoting a new version of the customs-compliance agent, the team wants to send 10% of live production traffic to it and keep 90% on the current version, comparing quality before committing further. Which Model Serving capability accomplishes this?
• The charter-desk assistant's External Models endpoint needs an API key for a third-party provider. Where should that credential be configured so it is never exposed in application code or in the endpoint's visible configuration?
• After registering a new version of the port-operations copilot, the Model Serving endpoint fails to load it with an error about an unexpected input field type. What is the most likely cause, and what should the engineer check first?
• Meridian Line wants to promote model versions to production only after they beat the current production version on the evaluation suite, using a repeatable pattern where the currently-trusted version and a candidate both have stable names the deployment pipeline can reference. Which pattern fits?
• An engineer logging the maintenance-triage agent with MLflow wants the Model Serving endpoint to reproduce the exact same Python library versions used during development, so the agent's tool-calling behavior does not subtly change in production. Which practice ensures this?
• Meridian Line's port-operations copilot logs full request and response payloads through AI Gateway for audit purposes, but some of those payloads contain a planner's personal contact details entered in free text. The compliance team wants the audit log retained while limiting who can see the raw PII inside it. Which combination of controls addresses this?
• Meridian Line's safety-review summarizer sees an unpredictable, spiky volume of requests — quiet for hours after an incident report is filed, then a burst of follow-up questions from the review board right after filing, then quiet again. Which Foundation Model APIs configuration best matches this traffic pattern while controlling cost?
• Meridian Line's data science group produces its own embedding vectors for crew-report text using a specialized multilingual model that runs outside Databricks, and wants to deploy a Vector Search index that simply stores and serves those pre-computed vectors without Databricks re-embedding anything. Which index type and deployment approach fits?
• A readiness reviewer checks the charter-desk assistant before its go-live date and finds the Model Serving endpoint is created and healthy, but queries return no retrieved clauses at all, even for questions that clearly match content in the source table. What is the most likely gap in the deployment, given that the chain code is unchanged from a working notebook test?
• Meridian Line's legal team wants the charter-desk assistant to call a specific third-party frontier model that Databricks does not host, but still wants the call routed through a governed Databricks endpoint so usage can be tracked and secrets stay off the client. What should the deployment team configure?
• Meridian Line's charter-desk assistant is a LangChain-style chain wrapped and logged as an MLflow pyfunc model without an explicit signature; MLflow inferred one from a quick local call. In staging, the Model Serving endpoint rejects real requests with a schema-validation error the notebook never showed. What is the most likely cause and correct fix?
• Meridian Line wants to add an LLM step to the customs-compliance agent that only needs to be available occasionally, at low and unpredictable request volume, and the team does not want to manage or pay for reserved capacity. Which Model Serving option best fits?
• A data engineer at Meridian Line finishes validating the port-operations copilot chain in a notebook and calls mlflow.logmodel(), then wants it available to the deployment team as a governed, versioned asset that Unity Catalog permissions can control. What must they do next?
• The port-operations copilot needs semantic search over the circulars-and-notices Delta table, and the team is assembling the full deployed application. Beyond writing the index-creation call, what infrastructure must exist first for a Delta Sync Vector Search index to be created against that table?
• Meridian Line's maintenance-triage agent has been validated in a development notebook. Given these remaining steps — (I) register the model to Unity Catalog, (II) log the chain as an MLflow pyfunc model with a signature and dependencies, (III) create or update a Model Serving endpoint pointing at the registered model version, (IV) run the notebook's chain object interactively to confirm it answers correctly — which ordering gets it into production correctly?
• Meridian Line's safety-review summarizer has three registered versions in Unity Catalog. The team wants the serving endpoint to always point at 'whichever version is currently blessed for production' without editing the endpoint config every time a new version is promoted. What Unity Catalog Model Registry feature accomplishes this?
• Meridian Line's charter-clause index needs to reflect new contracts within minutes of being written to the Delta table, since brokers cite clauses the same day a charterparty is signed. Which Vector Search sync-mode choice fits, and what is the trade-off against the alternative?
• Meridian Line's charter-desk assistant endpoint was accidentally called in a tight retry loop by a broken client script overnight, running up cost before anyone noticed. Which AI Gateway capability, configured on the endpoint, would have capped the damage automatically?
• Meridian Line's evaluation team wants to build a held-out test set from real production traffic on the safety-review summarizer endpoint, capturing the actual prompts sent and responses returned so they can later score them for groundedness. Which AI Gateway feature should be enabled on the endpoint to make this possible?
• Meridian Line wants to promote a new version of the customs-compliance agent cautiously: send it 10% of live traffic while the existing version keeps handling the rest, and watch quality before cutting over fully. What Model Serving capability supports this directly on one endpoint?
• The maintenance-triage agent's endpoint sits idle most nights when no vessels are in port, then gets a burst of queries each morning. Meridian Line wants to avoid paying for idle compute overnight but accepts a short cold-start delay on the first morning request. Which Model Serving configuration fits, and what is the trade-off?
• Meridian Line's crane-maintenance triage agent must respond to on-deck technicians within roughly a second, all shift long, every shift, and traffic volume is predictable and continuously high. Which Foundation Model APIs mode is the better production fit, and why?
• A Delta Sync Vector Search index over Meridian Line's port-authority circulars table has been running fine for months, but a new set of circulars written to the table today never shows up in search results, even after waiting well past the usual sync delay. What is the most likely cause?
• Meridian Line's data scientist logs the port-operations copilot chain with mlflow.logmodel(), but does not pass a condaenv, piprequirements, or extrapiprequirements argument, relying on MLflow's automatic dependency inference. When the model is deployed to a serving endpoint weeks later, it fails to start with an import error for a library the chain actually depends on. What most likely went wrong?
• Meridian Line's customs-compliance agent calls a tool that looks up HS-code tariff rates from an external reference table, and another tool that validates a declaration's required fields. When the team logs this agent with MLflow for deployment, what must they do beyond logging the LLM prompt chain itself?
• Query volume against Meridian Line's bill-of-lading Vector Search index has grown tenfold since launch, and terminal planners now report slow search responses during peak intake hours. The index's sync mode and embedding model are unchanged. What is the appropriate first lever to address the slowdown?
• Before fully cutting the safety-review summarizer over to a newly fine-tuned model version, Meridian Line wants real production requests scored by the new version's output without those outputs ever being shown to actual safety reviewers, purely to compare quality against the incumbent. Which deployment pattern fits, and how does it differ from traffic splitting?
• Meridian Line configures an External Models endpoint that routes the marine-insurance underwriting assistant's calls to a third-party provider's API. Where should the provider's API key live so it is never exposed in notebook code, application logs, or version control?
• Meridian Line's charter-desk assistant, deployed on Model Serving, needs the same third-party API key across dev, staging, and production endpoints, but the security team wants production's key to be rotated independently without touching the dev or staging configurations. How should this be structured?
• Six months after launch, Meridian Line's evaluation team wants to build a fine-tuning dataset from real customs-compliance agent interactions, but discovers the endpoint was never configured to retain request/response content — only aggregate token counts were ever available. What should have been configured from the start to make this possible?
• Meridian Line is assembling the deployed system for its port-operations copilot: a retrieval step over the circulars Vector Search index, a summarization step, and a separate charter-desk assistant that has nothing to do with circulars. Should these be deployed behind one shared Model Serving endpoint or separate endpoints, and why?
• A newly promoted version of the maintenance-triage agent, now serving 100% of traffic after a successful staged rollout, starts producing noticeably worse triage recommendations following an upstream data change. Meridian Line needs to get back to the previous known-good behavior immediately. What is the fastest correct action?
• Meridian Line's crew-report translation tool was logged and registered with a signature declaring a single string input and a single string output. The team now wants to pass a structured request (language code plus report text) instead, without changing the deployed endpoint's contract for existing callers. What must happen?