PMLE Practice Question: Collaborating Within and Across Teams to Manage Data and Models
An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?
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
✓
Time travel for accessing previous versions
Delta Lake provides time travel (B), allowing queries against earlier snapshots of a table via version numbers or timestamps, which directly supports the team's data versioning requirement. It also delivers schema enforcement and evolution (C), rejecting writes that violate the table schema while permitting controlled schema changes such as adding columns. Additionally, Delta Lake brings ACID transactions (D) to data lakes, ensuring atomic, consistent, isolated, and durable writes on top of object storage like GCS. Options A and E are not Delta Lake benefits: encryption at rest is handled by the underlying storage service (e.g., Google Cloud Storage), not Delta Lake itself, and Delta Lake is a storage/transaction layer rather than a built-in real-time streaming engine.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automatic data encryption at rest
Why it's wrong here
Delta Lake provides ACID transactions, time travel, and schema enforcement; encryption at rest is handled by the underlying storage or cloud platform, not the table format. It is tempting because data protection matters, but that requirement is met by disk or bucket encryption, not Delta Lake itself.
- ✓
Time travel for accessing previous versions
Why this is correct
Delta Lake's transaction log records every write as a numbered commit, so querying a prior version by timestamp or version number retrieves the exact historical snapshot. This directly satisfies the team's data versioning requirement on Dataproc, enabling reproducible ML training and rollback without duplicating datasets.
- ✓
Schema enforcement and evolution
Why this is correct
Delta Lake validates incoming writes against the table's declared schema, rejecting non-conforming records, while allowing controlled column additions via mergeSchema. This satisfies the versioning scenario by keeping each committed version structurally consistent, preventing silent corruption that would otherwise break downstream ML feature pipelines.
- ✓
ACID transactions on data lakes
Why this is correct
Delta Lake layers a transaction log over Parquet files, giving serialisable isolation and atomic commits across concurrent readers and writers on Dataproc. This satisfies the data versioning requirement because each version transition is all-or-nothing, so ML teams never observe partially written or inconsistent snapshots.
- ✗
Built-in real-time streaming
Why it's wrong here
Delta Lake provides ACID transactions, time travel and schema enforcement, not a streaming engine; ingestion relies on Structured Streaming or Kafka. It is tempting because Delta tables can be both batch and streaming sources and sinks, so real-time pipelines are a common use case, but the format itself supplies no built-in real-time streaming capability.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.