[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"me":3,"catalog:en:data-engineer\u002Fsenior":4,"config":126},null,{"field_key":5,"field_name":6,"seniority":7,"topic_key":8,"topic_name":8,"spec_key":8,"spec_name":8,"locale":9,"cell_total":10,"field_total":11,"seniorities":12,"topics":15,"specs":41,"samples":42},"data-engineer","Data Engineer","senior","","en",392,600,[13,14,7],"junior","mid",[16,20,23,26,29,32,35,38],{"key":17,"name":18,"count":19},"data-governance-lineage","Data Governance Lineage",75,{"key":21,"name":22,"count":19},"data-modeling-warehousing","Data Modeling Warehousing",{"key":24,"name":25,"count":19},"data-partitioning-scaling","Data Partitioning Scaling",{"key":27,"name":28,"count":19},"data-pipeline-design","Data Pipeline Design",{"key":30,"name":31,"count":19},"data-pipeline-orchestration","Data Pipeline Orchestration",{"key":33,"name":34,"count":19},"data-pipeline-reliability","Data Pipeline Reliability",{"key":36,"name":37,"count":19},"data-quality-validation","Data Quality Validation",{"key":39,"name":40,"count":19},"streaming-fundamentals","Streaming Fundamentals",[],[43,61,74,87,100,113],{"id":44,"topic":18,"difficulty":45,"body":46,"options":47,"correct_key":55,"explanation":60},"019f6a40-d6be-7c22-85ad-57d181e7ad97",3,"How does 'data ownership' typically differ from 'data stewardship'?",[48,51,54,57],{"key":49,"text":50},"a","Ownership is a purely technical role while stewardship is purely legal",{"key":52,"text":53},"b","They are two names for exactly the same responsibility",{"key":55,"text":56},"c","The owner is accountable overall; a steward handles day-to-day tasks",{"key":58,"text":59},"d","Stewardship only applies to datasets that contain PII","Ownership carries overall accountability for a dataset, while stewardship often covers the operational, day-to-day quality and definition work, frequently on the owner's behalf.",{"id":62,"topic":18,"difficulty":45,"body":63,"options":64,"correct_key":58,"explanation":73},"019f6a40-d6c3-7f36-ab61-319aa32238e8","A company deletes raw event data after 90 days per policy, but later discovers it needs that data to investigate a billing dispute from four months ago. What does this reveal?",[65,67,69,71],{"key":49,"text":66},"The lineage graph for the event data was incomplete",{"key":52,"text":68},"The data catalog entry for the event data was missing a description",{"key":55,"text":70},"The access control rules for the event data were too permissive",{"key":58,"text":72},"The retention policy lacked an exception for legitimate longer-retention needs","This shows the retention policy failed to account for a legitimate need to retain data longer for dispute investigation, or lacked an exception process.",{"id":75,"topic":18,"difficulty":45,"body":76,"options":77,"correct_key":52,"explanation":86},"019f6a40-d6c5-7e40-b70e-1be8fb1422d8","A table's schema changes from\n```\nbefore: order_id, amount\nafter: order_id, amount_cents\n```\nSeveral downstream reports still reference `amount`. Without a downstream impact analysis before the change, what is the most likely outcome?",[78,80,82,84],{"key":49,"text":79},"The reports will automatically convert amount_cents back to amount",{"key":52,"text":81},"The downstream reports will break or silently show wrong values",{"key":55,"text":83},"The change will have no effect since column renames are always backward compatible",{"key":58,"text":85},"The data catalog will block the schema change automatically","Without checking downstream impact first, reports that still reference the old column will break or silently show incorrect values.",{"id":88,"topic":18,"difficulty":45,"body":89,"options":90,"correct_key":49,"explanation":99},"019f6a40-d6c8-7237-8f34-43404b010faf","What is a common drawback of a purely centralized data governance model at a large organization?",[91,93,95,97],{"key":49,"text":92},"The central team can become a bottleneck for fast decisions",{"key":52,"text":94},"It guarantees every team will duplicate the same datasets",{"key":55,"text":96},"It removes the need for any data ownership at all",{"key":58,"text":98},"It automatically eliminates the need for a data catalog","A common downside of pure centralization is that the central team becomes a bottleneck for domain teams needing fast, context-specific decisions.",{"id":101,"topic":18,"difficulty":45,"body":102,"options":103,"correct_key":58,"explanation":112},"019f6a40-d6c9-7163-b614-ed40ad11b800","A large company has many independent domain teams, each best positioned to know their own data's quality rules, but the company still wants consistent classification and access standards company-wide. Which governance approach best fits this need?",[104,106,108,110],{"key":49,"text":105},"A fully centralized model where one team manually reviews every dataset",{"key":52,"text":107},"No governance model at all, letting each team improvise",{"key":55,"text":109},"A model where governance is only applied to the largest datasets",{"key":58,"text":111},"A federated model: central standards, domain teams own their own data","A federated model combines company-wide standards and shared tooling with domain teams owning their own data within that framework, balancing consistency and speed.",{"id":114,"topic":22,"difficulty":45,"body":115,"options":116,"correct_key":55,"explanation":125},"019f6a40-d6d8-7e6b-916b-04ce7da6fe91","A retail fact table is designed at 'one row per store per day' grain. Finance later asks for analysis of individual, transaction-level fraud patterns. Which statement is correct?",[117,119,121,123],{"key":49,"text":118},"The existing grain is sufficient, daily totals hold enough detail",{"key":52,"text":120},"Grain can be changed later with no impact, by adding columns",{"key":55,"text":122},"A new fact table at transaction grain is needed for this",{"key":58,"text":124},"Fraud detection should be done exclusively inside the dimension tables","Once data has been aggregated to a coarser grain, the finer-grained detail is gone; a transaction-level analysis requires a fact table built at transaction grain from the 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