Dataiku Developer Certification cheat sheet
Dataiku Developer Certification · Keep the key ideas within reach.
Dataiku code recipes, datasets, environments and extension boundaries
Flow dependency and notebook exploration are different roles.
dataiku and dataikuapi expose distinct interfaces.
Schema-changing writes require deliberate compatibility review.
Code environments and plugins do not independently prove secure execution.
Dataiku scenarios, variables, bundles and API-service deployment
Trigger evidence differs from successful actions and business outcomes.
Variable authority, persistence and syntax depend on context.
Project bundles and API services have different deployment paths.
Verify actual activated state, dependencies, permissions and resulting data.
Python and dataframe reasoning for applied analytics
Python and dataframe types affect transformation meaning.
Labels, positions, alignment and join multiplicity need explicit checking.
Notebook state does not replace reproducible execution.
Verify small-table results, missing-value choices, units and populations.
Intelligent search, learning and evaluation
Define states, actions, objective and goal test before choosing a search.
Breadth-first minimum-edge guarantees need equal edge costs.
A-star guarantees depend on heuristic and repeated-state handling assumptions.
Separate training, validation and final evaluation roles.
Precision and recall use different denominators; accuracy can conceal class imbalance.
Data, Databases, and Information Retrieval
Primary keys are unique and non-null; foreign keys enforce referential integrity.
WHERE filters rows before grouping; HAVING filters aggregated groups.
COUNT(*) counts rows; COUNT(column) skips NULL values.
LEFT JOIN preserves unmatched left rows; INNER JOIN removes them.
Precision = relevant retrieved / retrieved; recall = relevant retrieved / all relevant.
Identity, access and cryptographic protection
Identification, authentication, authorisation and accounting are four distinct steps.
MFA needs two different factor categories, not two instances of one.
RBAC suits stable job roles; ABAC suits context-dependent, fine-grained rules.
Kerberos uses a KDC with TGT and service tickets for SSO.
Symmetric ciphers handle bulk data; asymmetric ciphers solve key distribution and signatures.
Signing uses a specified signature scheme with the signer’s private key; verification and custody are essential to origin evidence.
ECB leaks patterns; use authenticated modes such as GCM or encrypt-then-MAC.
Security operations, monitoring and response
Asset inventory and configuration baselines underpin every operational control.
Changes need an appropriate authorization path, recovery planning and relevant evidence.
Emergency changes still require retrospective documentation within a defined window.
Monitor log sources for silence, not just for events.
Distinguish historical Revision 2 phases from current NIST incident-response guidance integrated with broader risk management.
Containment precedes eradication in almost every scenario.
Capture volatile evidence (memory, connections) before disk and before power-off.
Continuity, backup and technical recovery
Distinguish tolerated data loss from tolerated interruption.
A recovery point and restore time must both meet the stated targets.
A same-failure-domain snapshot alone does not cover loss of that domain.
Verify complete restore procedures and actual dependency behavior.