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Databricks Certified Data Engineer Associate certification is a highly sought-after certification in the data engineering industry. Databricks Certified Data Engineer Associate Exam certification demonstrates that a candidate has the knowledge and skills required to design and build data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is highly valued by employers in various industries.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a professional certification exam designed to measure the knowledge, skills, and abilities of data engineers who work with Databricks. Databricks is a cloud-based big data processing and analytics platform that is used by organizations of all sizes to manage large volumes of data and gain valuable insights. Databricks-Certified-Data-Engineer-Associate Exam is intended for data engineers who are responsible for designing, building, and maintaining data pipelines, data lakes, and data warehouses using Databricks.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
An organization has implemented a data pipeline in Databricks and needs to ensure it can scale automatically based on varying workloads without manual cluster management. The goal is to meet the company's Service Level Agreements (SLAs), which require high availability and minimal downtime, while Databricks automatically handles resource allocation and optimization.
Which approach fulfills these requirements?

A. Deploy Job Clusters with fixed configurations, dedicated to specific tasks, without automatic scaling.
B. Use Spot Instances to allocate resources dynamically while minimizing costs, with potential interruptions.
C. Use Serverless compute in Databricks to automatically scale and provision resources with minimal manual intervention.
D. Use Interactive Clusters in Databricks, adjusting cluster sizes manually based on workload demands.

Answer: C

Explanation:
Serverless compute in Databricks automatically provisions and scales resources to meet workload demands, ensuring high availability and minimal downtime while reducing the need for manual cluster management, which aligns with SLA requirements.

NEW QUESTION # 86
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

A. There is no way to identify the owner of the table
B. Review the Owner field in the table's page in the cloud storage solution
C. Review the Permissions tab in the table's page in Data Explorer
D. All of these options can be used to identify the owner of the table
E. Review the Owner field in the table's page in Data Explorer

Answer: E

Explanation:
he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.
Reference:
1: Data Explorer | Databricks on AWS
2: Table details | Databricks on AWS
3: Set owner when creating a view in databricks sql - Databricks - 9978
4: Table access control | Databricks on AWS
5: External tables | Databricks on AWS

NEW QUESTION # 87
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

A. sql
CREATE UDF city_custom_logic(city STRING)
RETURNS STRING
AS
SELECT CASE WHEN city = 'London' THEN 'LND' ELSE city END;
B. sql
DEFINE FUNCTION city_custom_logic(city STRING)
AS STRING
RETURN CASE WHEN city = 'London' THEN 'LND' ELSE city END;
C. sql
CREATE FUNCTION city_custom_logic(city STRING)
RETURNS STRING
RETURN CASE
WHEN city = 'London' THEN 'LND'
ELSE city
END;
D. sql
CREATE FUNCTION city_custom_logic(city STRING)
AS
IF city = 'London' THEN 'LND' ELSE city END;

Answer: C

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

NEW QUESTION # 88
A data engineer has configured a Lakeflow Job that runs daily to ingest customer transaction data from a legacy relational database. The extracted data must be written directly to a Unity Catalog table and be immediately queryable via SQL. The team needs to ensure full data lineage is preserved. Which action enables this data ingestion with direct landing in Unity Catalog, preserved lineage, and immediate SQL query ability?

A. Stage data in cloud storage via REST API, then load with a separate job
B. Use ODBC pooling to extract all records in one batch and write to /dbfs/
C. Extract to Pandas DataFrame, convert to Spark, then save() to external path
D. Use spark.read.format("jdbc") and write with saveAsTable("catalog.schema.table")

Answer: D

Explanation:
spark.read.format("jdbc") loads the relational data into a Spark DataFrame, and saveAsTable("catalog.schema.table") writes it directly as a Unity Catalog table that is immediately queryable with SQL. Unity Catalog captures lineage for the Databricks read/write operation.

NEW QUESTION # 89
Which of the following must be specified when creating a new Delta Live Tables pipeline?

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

Answer: D

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.
Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

NEW QUESTION # 90
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