Databricks SQL and Workflows

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What is the primary purpose of Databricks SQL?

To create reference architecture diagrams
To train machine learning models
To run quick ad hoc queries on data lakes (correct)
To execute pipelines in automated ways

What is the benefit of using Databricks Workflows and Jobs for ML?

To run quick ad hoc queries on data lakes
To define pipelines for computing features and training models (correct)
To build and share dashboards
To create visualizations for query results

What is the focus of the provided reference architecture for MLOps on the Databricks Lakehouse platform?

To cover the majority of use cases and ML techniques (correct)
To omit the finer details of iterative development cycles
To provide a comprehensive guide for all ML techniques
To highlight alternative approaches for all parts of the process

What is omitted from the provided reference architecture diagrams?

The finer details of iterative development cycles (A) Signup and view all the answers

What is the purpose of the 'deploy code' pattern in MLOps?

To implement a recommended pattern for deploying code (D) Signup and view all the answers

What is the relationship between Databricks Workflows and Delta Live Tables?

They are both used to execute pipelines in automated ways (C) Signup and view all the answers

What is a key benefit of a Data Lakehouse architecture?

Cost-effective and flexible data storage (A) Signup and view all the answers

What is the primary purpose of MLflow's Model Registry component?

To store and manage models across their lifecycle (B) Signup and view all the answers

Which of the following is NOT a component of MLflow?

Data Engineering (C) Signup and view all the answers

What is the purpose of MLflow's Tracking component?

To track experiments and compare model metrics (B) Signup and view all the answers

What type of data is typically stored in a Data Lakehouse?

All structured and unstructured data (A) Signup and view all the answers

What is the name of the architecture used to organize data in a Data Lakehouse?

Medallion architecture (D) Signup and view all the answers

What is indicated by marking data as dev, staging, or prod?

Data quality and reliability guarantees (B) Signup and view all the answers

How is access to data in each environment controlled?

Through table access controls and cloud storage permissions (A) Signup and view all the answers

What is the primary difference between assets in dev and prod environments?

Quality and freshness guarantees (A) Signup and view all the answers

What is a result of managing models and code separately in MLOps?

Multiple possible patterns for getting ML artifacts through staging and into production (B) Signup and view all the answers

What is labeled according to its origin in dev, staging, or prod execution environments?

Data (C) Signup and view all the answers

What is used to control access to data in each environment?

Table access controls and cloud storage permissions (B) Signup and view all the answers