Reading and Understanding Data Schemas Questions
Be able to look at a data schema (table structure) and understand what data is available and how tables relate. At the start of the assessment, you'll be given a schema. Spend a minute understanding it before writing queries. Understand primary keys, foreign keys, and data types.
HardTechnical
0 practiced
In a warehouse using SCD Type 2 dimensions you notice queries joining facts to dimension tables by effective ranges are slow. Propose schema and query strategies to optimize common analytical patterns that need the 'dimension attributes at event time', including indexing, surrogate keys, and pre-computed snapshots.
EasyTechnical
0 practiced
You're given the following simplified e-commerce schema:customers(customer_id PK integer, name varchar, email varchar, created_at timestamp)orders(order_id PK integer, customer_id integer FK -> customers(customer_id), total_amount decimal, placed_at timestamp)order_items(order_item_id PK integer, order_id integer FK -> orders(order_id), product_id integer, quantity integer, price decimal)Spend a minute examining the schema. List the primary keys and foreign keys, describe relationships between tables and their cardinality (one-to-many, many-to-one), and name which columns you would inspect first for data quality issues and why.
HardTechnical
0 practiced
For a near-real-time dashboard that requires sub-second response for simple counts and sums, discuss schema trade-offs between normalized OLTP-style tables and denormalized pre-aggregated tables. Cover freshness, write amplification, storage cost, and maintenance complexity in your recommendation.
MediumTechnical
0 practiced
A nightly ETL starts failing after an upstream change: the upstream table renamed column 'user_email' to 'email_address' and added 'signup_source'. Describe step-by-step how you'd detect which downstream datasets and dashboards break, triage the highest-priority failures, implement a fix, and communicate status to stakeholders.
HardTechnical
0 practiced
Write a SQL approach to detect temporal foreign-key violations: an orders table has created_at, and customers have deleted_at (nullable). Find orders where the referenced customer was deleted before the order's created_at, i.e., impossible historical states. Use Postgres-style SQL and explain assumptions.
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