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

Section	Objectives
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Data Governance and Quality	- Data access control and governance - Unity Catalog basics - Data quality concepts and management
Productionizing Data Pipelines	- Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs
Data Processing and Transformations	- User-defined functions (UDFs) - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization)
Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which of the following commands will return the location of database customer360?

A. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};
B. DROP DATABASE customer360;
C. DESCRIBE DATABASE customer360;
D. USE DATABASE customer360;
E. DESCRIBE LOCATION customer360;

Answer: C

Explanation:
The command DESCRIBE DATABASE customer360; will return the location of the database customer360, along with its comment and properties. This command is an alias for DESCRIBE SCHEMA customer360;, which can also be used to get the same information. The other commands will either drop the database, alter its properties, or use it as the current database, but will not return its location12. Reference:
DESCRIBE DATABASE | Databricks on AWS
DESCRIBE DATABASE - Azure Databricks - Databricks SQL

NEW QUESTION # 43
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

A. They can configure the clusters to autoscale for larger data sizes
B. They can use clusters that are from a cluster pool
C. They can configure the clusters to be single-node
D. They can use endpoints available in Databricks SQL
E. They can use jobs clusters instead of all-purpose clusters

Answer: B

Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
Reference:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 44
A data engineer is working in a Python notebook on Databricks to process data, but notices that the output is not as expected. The data engineer wants to investigate the issue by stepping through the code and checking the values of certain variables during execution.
Which tool should the data engineer use to inspect the code execution and variables in real-time?

A. SQL Analytics
B. Python Notebook Interactive Debugger
C. Cluster Logs
D. Job Execution Dashboard

Answer: B

NEW QUESTION # 45
A data engineer is reviewing the documentation on audit logs in Databricks for compliance purposes and needs to understand the format in which audit logs output events. How are events formatted in Databricks audit logs?

A. In Databricks, audit logs output events in a JSON format.
B. In Databricks, audit logs output events in a CSV format.
C. In Databricks, audit logs output events in a plain text format.
D. In Databricks, audit logs output events in an XML format.

Answer: A

Explanation:
Databricks audit logs output events in JSON format, which provides structured, machine-readable data suitable for compliance monitoring and integration with security tools.

NEW QUESTION # 46
An organization needs to share a dataset stored in its Databricks Unity Catalog with an external partner who uses a different data platform that is not Databricks. The goal is to maintain data security and ensure the partner can access the data efficiently.
Which method should the data engineer use to securely share the dataset with the external partner?

A. Using a third-party API to access the Delta table
B. Databricks-to-Databricks Sharing
C. Exporting data as CSV files and emailing them
D. Using Delta Sharing with the open sharing protocol

Answer: D

NEW QUESTION # 47
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