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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Lakehouse Platform Concepts10-15%- Explain data governance and security concepts
- Describe key Databricks Lakehouse platform components
- Understand the Lakehouse architecture and its benefits
Topic 2: Apache Spark Data Processing Fundamentals20-25%- Create and use Spark DataFrames
- Use Spark SQL for data processing
- Apply transformations and actions on DataFrames
- Work with structured data types (arrays, maps, structs)
Topic 3: Python for Data Engineering10-15%- Use PySpark for data processing
- Implement user-defined functions (UDFs)
- Work with Spark APIs in Python
Topic 4: Spark SQL and DataFrames15-20%- Handle null values and data quality
- Join and union DataFrames
- Write and execute Spark SQL queries
- Aggregate and group data
Topic 5: Delta Lake Fundamentals20-25%- Understand ACID transactions and time travel
- Write to and read from Delta tables
- Explain Delta Lake features and benefits
- Create and manage Delta tables
Topic 6: Data Pipeline Architecture15-20%- Monitor and optimize pipeline performance
- Implement incremental data processing
- Understand ELT vs ETL patterns
- Design data pipelines for batch and streaming

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
Which of the following data workloads will utilize a Gold table as its source?

Answer: E

Explanation:
A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. References: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.


NEW QUESTION # 50
A data engineer needs to conduct Exploratory Analysis on data residing in a database that is within the company's custom-defined network in the cloud. The data engineer is using SQL for this task.
Which type of SQL Warehouse will enable the data engineer to process large numbers of queries quickly and cost-effectively?

Answer: D


NEW QUESTION # 51
Which compute option should be chosen in a scenario where small-scale ad-hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

Answer: D

Explanation:
Serverless Compute is ideal for small-scale, frequent, ad-hoc Python scripts because it provisions automatically, scales quickly, and terminates when queries finish, minimizing operational overhead and costs.


NEW QUESTION # 52
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: A

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.
:
Databases and Tables
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 53
A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information. How should the data engineer achieve this using Databricks?

Answer: C

Explanation:
Lakehouse Federation allows Databricks to directly query external data sources like PostgreSQL and Azure Synapse without duplicating data. This ensures the report always uses up-to-date information while avoiding the overhead and cost of data movement.


NEW QUESTION # 54
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