Databricks-DA-Assoc Developing AI/BI Genie Spaces Practice Question
A data analyst is configuring an AI/BI Genie Space for a marketing team. The team frequently asks questions that require joining multiple tables, such as 'Which campaigns generated the most leads?' and 'What is the conversion rate by channel?' The analyst notices that Genie sometimes produces incorrect joins, leading to inflated numbers. Which TWO actions should the analyst take to improve Genie's join accuracy? (Choose two.)
⚠ Common exam trap
The trap here is focusing on performance optimizations like cluster size or caching, which do not address the semantic gap causing incorrect joins.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Define foreign key relationships in the Unity Catalog
To improve Genie's join accuracy, the analyst should define foreign key relationships in Unity Catalog and provide sample queries with correct joins. These actions give Genie the necessary metadata and examples to understand table relationships and generate accurate SQL, reducing errors like Cartesian products or mismatched keys.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define foreign key relationships in the Unity Catalog
Why this is correct
Defining foreign key relationships in Unity Catalog provides Genie with explicit join paths between tables. This helps Genie understand how tables relate, reducing the chance of incorrect joins. When Genie knows that 'campaign_id' in the leads table references 'campaign_id' in the campaigns table, it can generate the correct join condition, avoiding Cartesian products or mismatched keys.
- ✗
Enable caching for the tables used in the Genie Space
Why it's wrong here
Caching can improve query speed by storing results in memory, but it does not affect the correctness of join logic. Genie's join errors are semantic, not performance-related. Caching might make queries faster but will not fix incorrect joins that produce wrong results. The analyst needs to address the underlying join understanding.
- ✗
Increase the compute cluster size for the Genie Space
Why it's wrong here
Increasing compute cluster size affects query performance and concurrency but does not improve the accuracy of join logic. Genie's join errors stem from a lack of semantic understanding, not from insufficient compute resources. Scaling up the cluster will not teach Genie how tables relate or correct its SQL generation.
- ✓
Provide sample queries that demonstrate correct joins
Why this is correct
Sample queries that show correct join syntax and relationships serve as examples for Genie to learn from. By including queries that join the campaigns and leads tables on the appropriate keys, Genie can mimic these patterns when answering similar questions. This is especially effective for complex joins involving multiple tables or specific filtering conditions.
- ✗
Restrict the Genie Space to a single table to avoid joins
Why it's wrong here
Restricting to a single table would prevent Genie from answering questions that require joins, which is the opposite of what the marketing team needs. It might avoid join errors but at the cost of functionality. The goal is to enable correct joins, not to eliminate them. This approach would severely limit the usefulness of the Genie Space.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DA-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DA-Assoc exam.