Databricks-ML-Assoc Model Deployment Practice Question
A data scientist deploys a model to a Databricks Model Serving endpoint and enables inference tables. After a week, they want to analyze prediction drift by joining the logged requests with ground-truth labels that arrive later. Which statement describes how they should access the inference table data for this analysis?
⚠ Common exam trap
The trap here is assuming inference data is only available through monitoring dashboards or REST metrics, rather than as a queryable Delta table.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Query the inference table using the Delta table name shown in the endpoint's Serving UI, then join it with a labels table on a request identifier.
Inference tables persist request and response payloads as Delta tables, with a fully qualified name visible in the endpoint UI. Analysts can query them directly and join with delayed ground-truth labels using the included request ID and timestamps. This supports drift and accuracy analysis without custom logging. Lakehouse Monitoring can build on these tables for automated metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Read the endpoint's driver logs from the cluster's log delivery location, parse the JSON entries, and join them with the labels table.
Why it's wrong here
Model Serving endpoints run as managed serverless compute; they do not expose driver logs in the way interactive clusters do. Log delivery to cloud storage is used for cluster logs, not for serverless endpoint inference payloads. Parsing driver logs would also be brittle and incomplete. Inference tables are the designed, queryable store for request and response data.
- ✓
Query the inference table using the Delta table name shown in the endpoint's Serving UI, then join it with a labels table on a request identifier.
Why this is correct
Inference tables are stored as Delta tables in Unity Catalog or the Hive metastore, and the endpoint's UI exposes the fully qualified table name. Users can query them with SQL or Spark and join to downstream label tables using the request ID column that Databricks includes. This enables drift analysis and model monitoring. The table contains request payloads, responses, and metadata such as timestamps and model version.
- ✗
Call the endpoint's `/metrics` REST API to export inference records, then load them into a Spark DataFrame for joining with labels.
Why it's wrong here
Databricks Model Serving does not expose inference records through a `/metrics` REST endpoint. Metrics endpoints, where available, report operational metrics like latency and request counts, not full request and response payloads. Inference tables are the supported mechanism for capturing payload-level data. This option invents an API that does not provide the required records for drift analysis.
- ✗
Enable model monitoring on the endpoint and rely on its automatically generated drift metrics, which replace the need to join with ground-truth labels.
Why it's wrong here
Lakehouse Monitoring can compute drift metrics, but it does not substitute for joining ground-truth labels when measuring actual model accuracy. Drift metrics compare distributions, not outcomes. The scenario specifically requires joining logged requests with labels that arrive later, which requires access to the raw inference records. Monitoring complements, but does not replace, that join.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-ML-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-ML-Assoc exam.