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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits |
| Topic 2: Python for Data Engineering | 10-15% | - Use PySpark for data processing - Implement user-defined functions (UDFs) - Work with Spark APIs in Python |
| Topic 3: Delta Lake Fundamentals | 20-25% | - Understand ACID transactions and time travel - Write to and read from Delta tables - Create and manage Delta tables - Explain Delta Lake features and benefits |
| Topic 4: Spark SQL and DataFrames | 15-20% | - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries - Handle null values and data quality |
| Topic 5: Apache Spark Data Processing Fundamentals | 20-25% | - Create and use Spark DataFrames - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing |
| Topic 6: Data Pipeline Architecture | 15-20% | - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Implement incremental data processing - Monitor and optimize pipeline performance |
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NEW QUESTION # 162
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?
Answer: E
NEW QUESTION # 163
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day.
They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?
Answer: A
Explanation:
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. References: PySpark SQL Module, Python datetime Module, Databricks Jobs
NEW QUESTION # 164
Which file format is used for storing Delta Lake Table?
Answer: D
Explanation:
Delta Lake tables use the Parquet format as their underlying storage format. Delta Lake enhances Parquet by adding a transaction log that keeps track of all the operations performed on the table. This allows features like ACID transactions, scalable metadata handling, and schema enforcement, making it an ideal choice for big data processing and management in environments like Databricks.
References:Databricks documentation on Delta Lake: Delta Lake Overview
NEW QUESTION # 165
A data engineer uses the Databricks workspace UI with Unity Catalog enabled. In Data Explorer, they select catalog corp_marketing, then schema campaigns, and see table email_stats. The engineer must let the growth-analysts group read email_stats from their SQL warehouses, but not create, alter, or delete any objects in corp_marketing or campaigns. Which action sequence meets the requirement?
Answer: C
Explanation:
Reading a Unity Catalog table requires USE CATALOG on its catalog, USE SCHEMA on its schema, and SELECT on the table. These privileges allow read access without granting object creation or modification rights.
NEW QUESTION # 166
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?
Answer: B
Explanation:
The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.
NEW QUESTION # 167
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