Databricks Certified Data Engineer - Associate

Explanation

Correct Answer: A

Why Option A is correct:

1. High Concurrency clusters are specifically designed for scenarios with hundreds of pipelines and parallel updates:

   • They provide optimized cloud storage connections that maximize data throughput
   • They handle high concurrency workloads efficiently
   • They are built for scenarios with many concurrent users/jobs accessing the same data

2. The scenario described requires:

   • Near real-time processing
   • Hundreds of pipelines
   • Parallel updates of many tables
   • Extremely high volume and high velocity data

3. High Concurrency clusters address these requirements by:

   • Using optimized cloud storage connections (AWS S3, Azure Blob Storage, Google Cloud Storage)
   • Providing better throughput for concurrent operations
   • Handling many parallel file operations efficiently

Why other options are incorrect:

B. Partition ingestion tables by a small time duration:

• While partitioning can help with parallel writes, it doesn't address the core requirement of handling hundreds of concurrent pipelines
• Partitioning alone doesn't optimize cloud storage connections for high throughput

C. Configure Databricks to save all data to attached SSD volumes:

• Databricks primarily uses cloud object storage (S3, ADLS, GCS) for persistent storage
• Attached SSD volumes are typically for temporary/scratch space, not for persistent data storage
• This approach would not scale for extremely high volume data

D. Isolate Delta Lake tables in their own storage containers:

• While this might help with organization, it doesn't address throughput requirements
• Could actually increase complexity and potentially reduce performance due to more API calls

E. Store all tables in a single database:

• This would likely create contention and bottlenecks rather than improve throughput
• The Catalyst Metastore doesn't load balance based on database organization
• This approach contradicts best practices for large-scale data management

Key Takeaway: For scenarios with hundreds of pipelines, parallel updates, and extremely high volume/high velocity data, Databricks High Concurrency clusters are specifically designed to maximize throughput through optimized cloud storage connections and efficient handling of concurrent operations.