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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Data Processing Fundamentals | 20-25% | - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Use Spark SQL for data processing - Create and use Spark DataFrames |
| 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% | - Explain Delta Lake features and benefits - Write to and read from Delta tables - Understand ACID transactions and time travel - Create and manage Delta tables |
| Topic 4: 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 5: Data Pipeline Architecture | 15-20% | - Implement incremental data processing - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns |
| Topic 6: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries |
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NEW QUESTION # 267
A data engineer has a Python variable table_name that they would like to use in a SQL query.
They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
Answer: E
NEW QUESTION # 268
A data engineer is designing an ETL pipeline to process both streaming and batch data from multiple sources. The pipeline must ensure data quality, handle schema evolution, and provide easy maintenance. The team is considering using Delta Live Tables (DLT) in Databricks to achieve these goals. They want to understand the key features and benefits of DLT that make it suitable for this use case. Why is Delta Live Tables (DLT) an appropriate choice?
Answer: A
Explanation:
Delta Live Tables (DLT) is appropriate because it provides automatic data quality checks through expectations, built-in support for schema evolution, and a declarative approach to pipeline development, making it easier to build and maintain both streaming and batch ETL pipelines.
NEW QUESTION # 269
Which TWO items are characteristics of the Gold Layer?
Choose 2 answers
Answer: A,B
NEW QUESTION # 270
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
Answer: B
Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
References:Databricks documentation on SQL endpoints: SQL Endpoints in Databricks
NEW QUESTION # 271
A developer is building a data pipeline that processes records from a Bronze table into a Silver table. The Bronze table, bronze_events, contains duplicate records because of at-least-once delivery guarantees from the upstream ingestion system. The developer writes the following PySpark code:
deduped_df = df.dropDuplicates()
deduped_df.summary("count", "mean", "stddev").show()
from pyspark.sql.functions import approx_count_distinct
deduped_df.select(approx_count_distinct("user_id")).show()
After running dropDuplicates() without arguments, some rows that differ only in the event_timestamp column remain. The developer wants to deduplicate records based only on user_id and event_type, keeping one row for each unique combination of those two columns.
Which code change achieves this deduplication requirement?
Answer: D
NEW QUESTION # 272
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