Databricks-ML-Assoc ML Workflows Practice Question
Which TWO factors should be considered when choosing between Batch Inference and Real-time Inference for a model in Databricks?
⚠ Common exam trap
Candidates often over-focus on model accuracy or training speed, forgetting that deployment strategy is primarily dictated by the business requirements of latency and data throughput volume rather than model complexity.
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
✓
The required latency for the model predictions to reach the end user.
Choosing between batch and real-time inference depends on latency requirements and data availability. Batch is suited for high-volume, non-time-sensitive tasks, whereas real-time is necessary for low-latency, event-driven predictions. Understanding these trade-offs is essential for ML architects to design cost-effective and performant serving strategies that align with business needs, as they dictate the infrastructure configuration, monitoring complexity, and the overall deployment strategy for the model lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The memory limit of the driver node during the model training phase.
Why it's wrong here
Training memory limits are irrelevant to the choice of inference strategy. Batch vs. real-time inference decisions are driven by the application's latency needs and data processing patterns, not by the hardware constraints encountered during the model training phase, which occurs at a completely different stage in the lifecycle.
- ✓
The required latency for the model predictions to reach the end user.
Why this is correct
Latency is the primary driver for choosing between real-time and batch inference. If the user requires sub-second responses to an event, real-time serving is necessary. If the predictions can be processed asynchronously, batch inference is significantly more efficient and cost-effective for handling high volumes of data.
- ✓
The total volume and frequency of the incoming data requests.
Why this is correct
High-volume, sporadic data is better suited for batch processing, which optimizes compute utilization. Conversely, continuous, low-volume requests are ideal for real-time endpoints. The request pattern directly dictates the cost-benefit analysis of maintaining an always-on serving endpoint versus running scheduled batch jobs on ephemeral clusters.
- ✗
The number of features used in the machine learning model.
Why it's wrong here
The number of features affects the computation time per inference, not the choice of strategy. Whether you use ten features or one thousand, you still need to decide if you need the result immediately (real-time) or if it can wait for a scheduled process (batch).
- ✗
The programming language used to write the ML model training code.
Why it's wrong here
The training language (e.g., Python, R) does not influence the inference strategy. Both real-time serving and batch inference can support models written in any language supported by the Databricks environment. The decision should be based on operational requirements, performance SLAs, and the business application's usage pattern.
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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.