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| Section | Weight | Objectives |
|---|---|---|
| Apache Spark Data Processing Fundamentals | 20-25% | - Create and use Spark DataFrames - Apply transformations and actions on DataFrames - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) |
| Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing |
| Data Pipeline Architecture | 15-20% | - Implement incremental data processing - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns |
| Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Understand ACID transactions and time travel - Create and manage Delta tables - Write to and read from Delta tables |
| Spark SQL and DataFrames | 15-20% | - Write and execute Spark SQL queries - Handle null values and data quality - Aggregate and group data - Join and union DataFrames |
| Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components |
>> Latest Databricks-Certified-Data-Engineer-Associate Exam Answers <<
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NEW QUESTION # 134
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 # 135
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?
Answer: D
Explanation:
dbfs:/user/hive/warehouse Thereby showing "dbfs:/user/hive/warehouse/customer360.db The location of the customer360 database depends on the value of the spark.sql.warehouse.dir configuration property, which specifies the default location for managed databases and tables. If the property is not set, the default value is dbfs:/user/hive/warehouse. Therefore, the customer360 database will be located in dbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such as dbfs:/user/hive/database, then the customer360 database will be located in dbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.
Option B is not correct, as dbfs:/user/hive/warehouse is the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with .db to the directory path, such as dbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.
Reference:
Databases and Tables
[Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 136
What is the maximum output supported by a job cluster to ensure a notebook does not fail?
Answer: A
Explanation:
The maximum output supported by a job cluster in Databricks is 10MB. If the output exceeds this limit, the notebook may fail.
NEW QUESTION # 137
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?
Answer: A
Explanation:
Delta Live Tables is a declarative framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling. Delta Live Tables supports both SQL and Python as the languages for defining your datasets and expectations. Delta Live Tables also supports both streaming and batch sources, and can handle both append-only and upsert data patterns. Delta Live Tables follows the medallion lakehouse architecture, which consists of three layers of data: bronze, silver, and gold. Therefore, migrating to Delta Live Tables does not require any of the changes listed in the options B, C, D, or E. The data engineer and data analyst can use the same languages, sources, and architecture as before, and simply declare their datasets and expectations using Delta Live Tables syntax. Reference:
What is Delta Live Tables?
Transform data with Delta Live Tables
What is the medallion lakehouse architecture?
NEW QUESTION # 138
Which of the following describes the relationship between Gold tables and Silver tables?
Answer: D
NEW QUESTION # 139
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