12 Tough Data‑AI Interview Questions from Leading Companies – Can You Master the Latest Trends? (Part 1)

Introduction

2026 marks the year of large‑scale Agent deployment and the beginning of the "Data Agent Ready" era. The article collects twelve high‑value interview questions that data‑x‑AI practitioners have encountered at leading companies over the past year, highlighting fast‑evolving trends that readers can use to identify knowledge gaps.

Q1. What’s the difference between an “AI Ready” warehouse and a “Data Agent Ready” warehouse?

AI Ready (2025) : databases and data warehouses treat vector retrieval and AI functions as standard features, storing embeddings, computing similarity, and allowing model calls inside the warehouse. Users remain human analysts.

Data Agent Ready (2026) : with coding agents like Cursor and Claude Code and data‑analysis agents becoming common, the warehouse’s users turn into agents. Design philosophy shifts to meet agents’ distinct safety, consistency, and massive‑scale processing requirements.

In short, AI Ready means “data developers use AI”; Data Agent Ready means “AI uses data”.

Q2. How does the shift of the “service object” from humans to models affect data/warehouse development?

Traditionally, warehouses (ODS/DWD/DWS) serve analysis and decision‑making. When data faces large language models, the challenge becomes converting enterprise data into model‑perceivable, searchable, and executable context.

Data still originates from the warehouse but is no longer consumed in its original form. Developers must extend their responsibilities: engineering data so that models can perceive, understand, and act on it, beyond merely writing business SQL.

Q3. What principles should guide AI‑oriented metadata and lineage collection?

The core principle is "machines handle facts, humans handle semantics" :

Automatic collection : technical metadata, runtime lineage, field‑level lineage (SQL parser/execution plan/OpenLineage), quality metadata, metadata access logs.

Manual maintenance + review : business metadata, owners/tags (semi‑automatic recommendation + human confirmation).

Lineage must distinguish static lineage (design‑time, derived from SQL files, manifests, DAGs) from runtime lineage (actual execution events, Spark/Flink plans, query history). Both are required; relying on only one misses dynamic branches, temporary tables, or unseen tasks.

Lineage should also extend to consumption endpoints such as dashboards, metric stores, feature stores, ML models, and RAG knowledge bases.

Q4. How does a lakehouse format support AI? Example with Paimon for multimodal ingestion.

Reference answer : Using Paimon, a single table can store structured columns, image binaries (BLOB), and embedding vectors. Declaring a vector index at table creation enables a closed loop of "ingest → generate vector → build vector index → semantic search".

Typical Flink SQL DDL (shown below) illustrates this approach:

CREATE TABLE my_catalog.my_db.product_images (
    product_id BIGINT,
    name STRING,
    description STRING,
    category STRING,
    image BYTES,               -- stores image binary or external URL
    embedding ARRAY<FLOAT>      -- stores CLIP‑generated vectors
) PARTITIONED BY (category)
WITH (
    'blob-field' = 'image',
    'global-index.btree.index-column' = 'product_id',   -- scalar filter acceleration
    'global-index.lumina-vector-ann.index-column' = 'embedding' -- vector ANN index
);

For data‑warehouse developers, this means they no longer need a separate vector store for RAG or feature‑store scenarios; the lakehouse itself can store and govern both raw data and vector representations.

Q5. Which features of Iceberg or other lake‑storage frameworks are most valuable for "AI + Data Warehouse"?

Three key Iceberg capabilities:

Deletion Vectors : enable row‑level deletes without rewriting whole files, delivering up to 10× faster CDC pipelines and reducing cost.

Row‑level Lineage : combined with deletion vectors, CDC becomes a native table capability, allowing downstream processes to handle only truly changed rows.

VARIANT type : treats logs, API responses, clickstreams, and other semi‑structured data as first‑class citizens, eliminating the need for flattening or separate systems and avoiding schema migrations when new fields appear.

Other frameworks offer similar abilities; readers should check the latest updates of the components they use.

Conclusion

This is the first half of the series; further questions and answers will be shared in subsequent parts.