Databricks-GenAI-Assoc Design Applications Practice Question
Which approach is recommended for managing secrets, such as API keys for external LLM providers, in Databricks?
⚠ Common exam trap
Candidates often suggest environment variables or hardcoding secrets in notebooks, mistakenly believing these are acceptable for internal development, which violates basic Databricks security compliance standards.
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
✓
Storing keys in Databricks Secrets.
Databricks Secrets provides a secure, encrypted storage mechanism for sensitive information, preventing credentials from being hardcoded in notebooks or scripts. This is a foundational security best practice. By using scope-based secret management, you ensure that only authorized users or service principals can access the credentials, keeping your RAG application secure and compliant with enterprise security standards.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hardcoding keys in a configuration file within a Repo.
Why it's wrong here
Hardcoding keys in configuration files is a severe security vulnerability. These keys can be exposed in version control systems (like Git), leading to credential theft. This practice violates basic security principles and increases the risk of unauthorized access to your external LLM provider accounts.
- ✓
Storing keys in Databricks Secrets.
Why this is correct
Databricks Secrets provides an encrypted, centralized way to manage sensitive keys. By using 'dbutils.secrets.get', you can inject these values into your code at runtime without them ever appearing in cleartext within your scripts, notebooks, or version control, ensuring a robust security posture for your production application.
- ✗
Passing keys as plain text arguments in a job parameter.
Why it's wrong here
Passing keys as plain text arguments in job parameters makes them visible in the Databricks UI and log files. Anyone with access to the job history can view these keys, which effectively defeats the purpose of credential management and exposes your sensitive credentials to unauthorized inspection and misuse.
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Storing keys in a public S3 bucket with restricted access.
Why it's wrong here
Storing keys in S3, even with access controls, is not the recommended way to manage secrets within the Databricks ecosystem. It adds unnecessary complexity and external dependencies. Databricks Secrets is purpose-built to handle these credentials with built-in encryption and access management, making it the superior architectural choice.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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-GenAI-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-GenAI-Assoc exam.