Intermediate~4h
Data Validation & Quality in MLOpsInterview Readiness Check
Great Expectations, Soda Core, PyDeequ, data contracts, schema evolution, and validation integration.
great-expectationssodadeequdata-contractsschema-validationdata-quality
Learning path
Feature Engineering Pipelines→Data Validation & Quality ← you are here→Monitoring & ObservabilityData Versioning & Lineage
Try a sample question
A model predicting loan defaults has been in production for three months. Its accuracy metric reported in dashboards stays constant, but a compliance audit finds the model has been silently outputting 'approved' for every application for two weeks. What type of failure is this?
A.A silent data failure where a feature column started arriving as null, triggering a default prediction path
B.A serving infrastructure failure where the GPU ran out of memory
C.An overfitting failure because the model memorized the training set
D.A model accuracy regression caused by concept drift
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of data validation & quality fundamentals
- Trade-offs and design decisions in real system scenarios
- great-expectations and soda — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Data Validation & Quality check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Data Validation & Quality check test?
- The check covers great expectations, soda core, pydeequ, data contracts, schema evolution, and validation integration. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Data Validation & Quality?
- Key concepts include: great-expectations, soda, deequ, data-contracts, schema-validation, data-quality. These frequently appear in mlops interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Data Validation & Quality?
- Most engineers spend around 4 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Data Validation & Quality?
- Data Validation & Quality builds on Feature Engineering Pipelines. Complete those first for the best results.
- Is great-expectations, soda, deequ covered?
- Yes. The Data Validation & Quality module covers great-expectations, soda, deequ among other key concepts. The check includes questions that reflect real mlops interview patterns.