Lecture Notes: Databricks Certified Data Engineer Professional Exam Question Analysis

Introduction

• Discusses issues with the quality of Databricks exam questions.
• Compares Databricks exam questions to AWS exam questions.
• Emphasizes the cost of the Databricks exam and the expectation for better quality questions.
• Expresses frustration with questions focusing on UI elements rather than knowledge testing.

Exam Question Analysis

Question: Diagnosing Transient Processing Delay

• Context: Schema update to include current timestamp, Kafka topic, and partition.
• Key Point: Limitation faced is that new columns in Delta Lake will remain null for existing data.
• Solution: Use separate processes to handle missing values or derive new column values from existing data.

Question: Sharing Data with Sales Organization

• Problem: Mismatched field names and fields not approved for sharing.
• Solution: Create a view to alias and present approved fields with minimal complexity.

Question: Proper Utilization of VM Resources

• Key Indicator: CPU utilization around 75% is ideal.
• Tip: Monitor a combination of metrics for cluster health.

Question: Type of Test for Area Under a Curve

• Answer: Unit test, as it focuses on the smallest testable part of an application.

Question: Spark Configuration in Databricks

• True Statement: Spark configurations set in the Clusters UI affect all notebooks attached to that cluster.

Question: Sharing Code Updates Safely

• Solution: Create a new branch from the main branch and commit changes to ensure no overwriting.

Question: Indicators of Partition Spilling to Disk

• Indicators: Executor logs and stage detail screen provide insights into partition spilling.

Question: Handling Duplicate Entries in Structured Streaming

• Solution: Use a watermark to manage data processing and deduplication within micro-batches.

Question: Migrating Workload from RDBMS to Databricks

• Consideration: Lack of enforcement of foreign key constraints in Delta Lake.

Question: Type 1 vs Type 2 Table Decision

• Key Information: Scalability concerns with Delta Lake's time travel capabilities.

Question: View Definition with Group Membership

• Result: Non-auditing members will see records for age greater than 17.

Question: Propagating Delete Requests Across Tables

• Key Point: Use of vacuum for physical deletion of data not just logical.

Question: External Database Connection using Secrets

• Access Control: Set read permissions at the secret scope level for secure access.

Question: Spark UI Indicators for Cached Table Performance

• Indicator: RDD blocks with the * annotation indicate failures in caching.

Question: First Line of Databricks Python Notebook

• Answer: %python is the first line.

Question: Key Benefit of End-to-End Testing

• Benefit: Simulates real-world usage of the application.

Question: Globally Unique ID in Job Run

• Definition: Run ID is a unique identifier for each job execution.

Question: Applying a Model to Data Frame

• Solution: Use the .select() method to apply model predictions correctly.

Question: Efficient Propagation of Batch Data

• Solution: Use Delta Lake's change data feed capability for efficient batch processing.

Question: Delta Lake Optimized Writes

• Description: Shuffling data before writing to group similar data together.

Question: Default Execution Mode for Autoloader

• Mode: Directory listing mode is used by default.

Question: Good Candidate for Partitioning in Delta Lake

• Choice: Use date column for partitioning due to common query patterns.

Question: Implementing a Near Real-Time Solution

• Solution: Partition tables by short time intervals for massive parallelism.

Question: Installing Python Package at Notebook Level

• Command: Use %pip install for notebook-scoped package installation.

Question: Cluster Configuration Resilience

• Best Configuration: Use 16 VMs to ensure resilience to failures with fine-grained executors.

Question: Command to Remove Before Scheduling a Job

• Command: Remove the display command for non-interactive job execution.

Question: Job Scheduling to Meet SLA

• Configuration: Use job clusters with hourly triggers to minimize costs.

Question: Efficient Dashboard Data Refresh

• Solution: Use nightly batch jobs to pre-calculate and store summary metrics.

Question: Updating Streaming Job with New Field

• Step: Update checkpoint location for the streaming job to ensure compatibility with changes.

Question: Optimizing Storage Costs for Streaming Jobs

• Adjustment: Set a longer trigger interval to reduce API call frequency and storage costs.