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

Section	Weight	Objectives
Topic 1: Python for Data Engineering	10-15%	- Use PySpark for data processing - Implement user-defined functions (UDFs) - Work with Spark APIs in Python
Topic 2: 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
Topic 3: Apache Spark Data Processing Fundamentals	20-25%	- Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Apply transformations and actions on DataFrames - Create and use Spark DataFrames
Topic 4: Data Pipeline Architecture	15-20%	- Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Implement incremental data processing - Design data pipelines for batch and streaming
Topic 5: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Join and union DataFrames - Write and execute Spark SQL queries - Aggregate and group data
Topic 6: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q318-Q323):

NEW QUESTION # 318
Which of the following must be specified when creating a new Delta Live Tables pipeline?

A. A location of a target database for the written data
B. At least one notebook library to be executed
C. A key-value pair configuration
D. A path to cloud storage location for the written data
E. The preferred DBU/hour cost

Answer: B

Explanation:
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode. Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.

NEW QUESTION # 319
A data engineer investigates a slow stage in the Databricks Spark UI. The engineer scrolls down to the Summary Metrics section for task durations and observes the following metrics for that stage:
25th-percentile duration: 40 seconds
Median duration: 45 seconds
75th-percentile duration: 50 seconds
Maximum duration: 80 seconds
What is the correct interpretation of these metrics?

A. The stage likely suffers from data skew.
B. There is a garbage-collection issue.
C. The stage is healthy.
D. The cluster is under-provisioned.

Answer: A

NEW QUESTION # 320
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

A. SUM
B. WHERE
C. PIVOT
D. CONVERT
E. TRANSFORM

Answer: C

Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
Reference:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
6: [SUM | Databricks on AWS]

NEW QUESTION # 321
Which of the following describes a scenario in which a data team will want to utilize cluster pools?

A. An automated report needs to be runnable by all stakeholders.
B. An automated report needs to be version-controlled across multiple collaborators.
C. An automated report needs to be refreshed as quickly as possible.
D. An automated report needs to be made reproducible.
E. An automated report needs to be tested to identify errors.

Answer: C

Explanation:
Cluster pools in Databricks are used to ensure that a set of pre-warmed clusters is readily available to run workloads. This means that when a job is submitted, it can be executed more quickly because there is no need to wait for a cluster to spin up. Therefore, if a data team needs to refresh an automated report as quickly as possible, they will want to utilize cluster pools to ensure that the job can be executed as quickly as possible.

NEW QUESTION # 322
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

A. Function
B. Temporary view
C. View
D. Database
E. Table

Answer: E

Explanation:
A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. Reference:
Create a table
Create a table from a query result
Register a table from a DataFrame
[Query a table]
[Update, delete, or merge into a table]

NEW QUESTION # 323
......

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