Assess the cost-effectiveness of Facebook's investment in machine learning-based content recommendation engines using standard costing.

Cost Components	Fixed Costs ($)	Variable Costs per Engine ($)	Engine Volume (engines)
ML-Based Content Recommendation	35,000,000,000	500,000	70,000

Requirements:

Calculate the total project cost using standard costing principles.

Determine the break-even point for recommendation engines.

Analyze the impact of technological advancements on development costs.

Conduct a sensitivity analysis on market demand and competition.

Evaluate the potential for improving content engagement through ML.

Provide recommendations for optimizing standard costing in ML technology.