Databricks-ML-Pro Model Deployment Practice Question
A data science team is preparing to deploy a high-throughput recommendation model using Databricks Model Serving. Which TWO factors must be considered to optimize endpoint latency and resource utilization? (Choose two)
⚠ Common exam trap
Candidates choose data storage options or training parameters instead of focusing on runtime compute configurations and scaling boundaries that directly affect latency and throughput.
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
✓
Configuring the minimum and maximum number of concurrent scaling units based on expected query traffic spikes.
Selecting appropriate instance types with GPU acceleration and configuring autoscaling bounds based on traffic patterns directly dictate inference latency and operational cost. These considerations are critical in production because underprovisioned endpoints cause timeout errors while overprovisioned endpoints waste cloud computing resources unnecessarily.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configuring the minimum and maximum number of concurrent scaling units based on expected query traffic spikes.
Why this is correct
Configuring scaling units correctly allows Databricks Model Serving to automatically scale compute resources up during traffic surges and scale down to zero during idle periods. This balance prevents latency degradation under heavy load while minimizing unnecessary cloud expenditure when request volumes drop.
- ✗
Converting all feature lookup tables from Delta Lake format directly into local CSV files inside the serving container.
Why it's wrong here
Converting dynamic Delta Lake feature tables into static CSV files inside serving containers introduces severe staleness issues and limits scalability. Model serving architectures should fetch features dynamically via online feature stores rather than baking static files into container images.
- ✓
Selecting the appropriate CPU or GPU compute instance type matching the model's computational complexity and memory footprint.
Why this is correct
Matching the instance type to the model architecture ensures efficient tensor operations for deep learning models or fast vector processing for tree-based models. Inappropriate instance sizing either leads to out-of-memory errors or fails to leverage hardware acceleration capabilities.
- ✗
Enabling Apache Spark adaptive query execution on the serving endpoint cluster to optimize joins during scoring.
Why it's wrong here
Databricks Model Serving endpoints do not execute Spark queries or run Adaptive Query Execution during inference request processing. Serving endpoints are lightweight scoring containers running optimized frameworks like ONNX, PyTorch, or Triton rather than distributed Spark compute engines.
- ✗
Ensuring the MLflow model uses pickle serialization exclusively because it outperforms all other serialization formats.
Why it's wrong here
Pickle serialization is notoriously insecure and often slower for large numeric arrays compared to optimized formats like MLmodel custom flavors, ONNX, or TorchScript. Modern serving architectures avoid raw pickle files to mitigate arbitrary code execution security vulnerabilities.
About these practice questions
This Databricks-ML-Pro question is part of Courseiva's 300-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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-Pro 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-Pro exam.