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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Spark SQL and DataFrames | 15-20% | - Join and union DataFrames - Write and execute Spark SQL queries - Handle null values and data quality - Aggregate and group data |
| Topic 2: Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Implement incremental data processing - Monitor and optimize pipeline performance |
| Topic 3: Apache Spark Data Processing Fundamentals | 20-25% | - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Apply transformations and actions on DataFrames |
| Topic 4: Delta Lake Fundamentals | 20-25% | - Create and manage Delta tables - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Write to and read from Delta tables |
| Topic 5: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components |
| Topic 6: Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing |
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NEW QUESTION # 102
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
Answer: B
NEW QUESTION # 103
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks-managed retention policies.
Which table type should the data engineer create for these ingestion tables?
Answer: C
Explanation:
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table, the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly. External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files. Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
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NEW QUESTION # 104
A data engineer needs to develop integration tests for an ETL process and deploy a version- controlled, packaged workflow into production using an external job scheduler. Which tool should the data engineer use for this job?
Answer: A
Explanation:
Databricks Asset Bundles enable packaging of workflows with version control, allowing integration testing and deployment into production while supporting orchestration by external job schedulers.
NEW QUESTION # 105
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?
Answer: C
Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
The use of higher-order functions like FILTER can be referenced from Databricks documentation on Higher- Order Functions.
NEW QUESTION # 106
What are the transformations typically included in building the Bronze layer ?
Answer: B
Explanation:
In the Medallion Architecture promoted by Databricks, the Bronze layer represents the raw ingestion layer where data is stored in its original form with minimal transformation. The primary goal is to ensure data fidelity and traceability , not to apply business logic or heavy transformations. Typical operations in the Bronze layer include schema inference (or enforcement), ingestion of raw data, and the addition of metadata columns such as ingestion timestamp, load date/time, and process identifiers. These metadata fields help track lineage, enable debugging, and support incremental processing downstream. Extensive data cleansing (A), aggregations (B), and business transformations (C) are intentionally deferred to the Silver and Gold layers , where data is refined and structured for analytics. By keeping Bronze data close to the source and append-only, organizations maintain a reliable audit trail and can reprocess data if needed. Therefore, adding operational metadata like load timestamps and process IDs is the correct characteristic of Bronze layer transformations.
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NEW QUESTION # 107
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