Study Databricks ML-PRO Distributed Training: key concepts, common traps, and exam decision cues.
On this page
Distributed-training questions are usually about fit, not loyalty. Databricks wants the parallelization strategy that matches the model, data, and infrastructure constraint.
Parallelization map
Requirement
Better first instinct
scale Spark-native ML pipelines across data
Spark-oriented distributed workflow
use a distributed training framework outside core SparkML patterns
Ray may be the better lane
larger data with same model structure
data parallelism candidate
model too large or complex for simple data-only split
evaluate model parallelism or another scaling strategy
cost or simplicity may beat more nodes
compare vertical and horizontal scaling trade-offs
What the exam is really testing
If the stem says…
Strong reading
“vertical vs horizontal scaling”
cost, complexity, and workload fit all matter
“parallelization strategies”
distinguish model parallelism from data parallelism
“compare Ray and Spark”
the right answer depends on workload and framework fit, not personal preference
Decision order that usually wins
Name the bottleneck first: data size, model size, training time, or ecosystem fit.
Decide whether the scale problem is solved by bigger nodes or more nodes.
Separate data parallelism from model parallelism before picking a framework.
Match Spark or Ray to the surrounding workflow and training pattern.
Prefer the simpler scaling path when it solves the real constraint.
The exam is not asking which framework is cooler. It is asking whether you can identify the actual scaling problem and choose a proportionate distributed-training design.
Scenario triage
Scenario
Better first move
tabular workflow already lives in Spark-native data processing
start with Spark-oriented distribution
training pattern depends on a distributed framework outside core SparkML lanes
evaluate Ray
same model must consume far more training data
consider data parallelism
model itself is too large for one worker to hold efficiently
consider model-parallel or alternate scaling strategy
question emphasizes cost and coordination overhead
compare vertical against horizontal scaling honestly
Common traps
Trap
Better rule
assuming more nodes always wins
scaling has cost and coordination trade-offs
treating Spark and Ray as identical
they support different patterns and ecosystems
picking a parallelization strategy without naming the bottleneck