The Looker LookML Developer exam assessed one’s ability to:

• Maintain and debug LookML code
• Build user-friendly Explores
• Design robust models
• Define caching policies
• Understand various datasets and associated schemas
• Use Looker tools such as Looker IDE, SQL Runner, & LookML Validator

Course content

Model management

Troubleshoot errors in existing data models.

• Determine error sources
• Apply procedural concepts to resolve errors

Apply procedural concepts to implement data security requirements.

• Implement permissions for users
• Decide which Looker features to use to implement data security (e.g., access filters, field-level access controls, row-level access controls)

Analyze data models and business requirements to create LookML objects.

• Determine which views and tables to use
• Determine how to join views into Explores
• Build project-based needs (e.g., data sources, replication, mock reports provided by clients)

Maintain the health of LookML projects in a given scenario.

• Ensure existing contents are working (e.g., use Content Validator, audit, search for errors)
• Resolve errors

Customization

Design new LookML dimensions or measures with given requirements.

• Translate business requirements (specific metrics) into the appropriate LookML structures (e.g., dimensions, measures, and derived tables)
• Modify existing project structure to account for new reporting needs
• Construct SQL statements to use with new dimensions and measures

Build Explores for users to answer business questions.

• Analyze business requirements and determine LookML code implementation to meet requirements (e.g., models, views, join structures)
• Determine which additional features to use to refine data (e.g., sql_always_where, always_filter, only showing certain fields using hidden: fields:, etc.)

Optimization

Apply procedural concepts to optimize queries and reports for performance.

• Determine which solution to use based on performance implications (e.g., Explores, merged results, derived tables)
• Apply procedural concepts to evaluate the performance of queries and reports
• Determine which methodology to use based on the query and reports performance sources (e.g., A/B testing, SQL principles)

Apply procedural concepts to implement persistent derived tables and caching policies based on requirements.

• Determine appropriate caching settings based on data warehouse’s update frequency (e.g., hourly, weekly, based on ETL completion)
• Determine when to use persistent derived tables based on runtime and complexity of Explore queries, and on users’ needs
• Determine appropriate solutions for improving data availability (e.g., caching query data, persisting tables, combination solutions)

Quality

Implement version control based on given requirements.

• Determine appropriate setup for Git branches (e.g., shared branches, pull from remote production)
• Reconcile merge conflicts with other developer branches (e.g., manage multiple users)
• Validate the pull request process

Assess code quality.

• Resolve validation errors and warnings
• Utilize features to increase usability (e.g., descriptions, labels, group labels)
• Use appropriate coding for project files (e.g., one view per file)

Utilize SQL Runner for data validation in a given scenario.

• Determine why specific queries return results by looking at the generated SQL in SQL Runner
• Resolve inconsistencies found in the system or analysis (e.g., different results than expected, non-unique primary keys)
• Optimize SQLs for cost or efficiency based on business requirements

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