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Databricks Certified Data Engineer Associate certification is ideal for professionals working in data engineering, data warehousing, and data modeling roles. Databricks Certified Data Engineer Associate Exam certification demonstrates the candidate's knowledge of Databricks and their ability to design and implement data engineering solutions using Databricks. It also validates their understanding of data transformation, ETL processes, data warehousing, and data modeling concepts. Databricks Certified Data Engineer Associate Exam certification can enhance the candidate's career opportunities and increase their earning potential.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

A. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
B. The Data Engineer should add print statements to find out what the variable is.
C. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted
D. The Spark User interface has a debug tab that contains the variables that are used in this session.

Answer: A

NEW QUESTION # 63
Which two items are characteristics of the Gold Layer? (Choose two.)

A. Read-optimized
B. Raw Data
C. Historical lineage
D. De-normalised
E. Normalised

Answer: A,D

Explanation:
The Gold layer contains de-normalized, business-level tables that are read-optimized for analytics, dashboards, and reporting, providing fast and simplified data access.

NEW QUESTION # 64
A data engineer is standardizing repository layouts for multiple teams adopting Databricks Asset Bundles. The engineer wants to ensure every project has a single authoritative configuration file at the repository root that defines the bundle name, targets, workspace settings, permissions, and resource mappings (for jobs and pipelines).
Which strategy should the data engineer use to meet this goal?

A. Place exactly one databricks.yml at the repository root; it is the main configuration file and may reference additional configuration files via the include mapping.
B. Place a databricks.yml in a .databricks/ hidden folder at the repository root; only hidden locations are valid for bundle configs.
C. Place multiple databricks.yml files under each subfolder (for example, jobs/, pipelines/, workspace/) and merge them at deploy time using the include mapping.
D. Place a databricks.yml at the repository root and optional databricks.yml in subfolders; the CLI prefers .yaml over .yml when both exist.

Answer: A

Explanation:
In Databricks Asset Bundles, the databricks.yml file located at the repository root serves as the single authoritative configuration file for the entire project. This file defines key elements such as the bundle name, deployment targets (for example, dev, test, prod), workspace configurations, permissions, and resource definitions including jobs and pipelines. Databricks documentation specifies that there must be one primary databricks.yml file at the root, ensuring consistency and simplifying deployment processes. However, to support modularity and maintainability, this root configuration file can reference additional YAML files using the include mapping. This allows teams to organize configurations across directories while still maintaining a single source of truth. Option A is incorrect because multiple root-level configuration files are not supported as independent authorities. Option C is invalid since configuration files are not restricted to hidden directories. Option D introduces ambiguity and contradicts the requirement for a single authoritative configuration. Therefore, using one root-level configuration file with optional includes is the correct and recommended approach.

NEW QUESTION # 65
An organization is looking for an optimized storage layer that supports ACID transactions and schema enforcement. Which technology should the organization use?

A. Cloud File Storage
B. Delta Lake
C. Unity Catalog
D. Data lake

Answer: B

Explanation:
Delta Lake is the optimized storage layer that adds ACID transactions, schema enforcement, and reliability on top of cloud object storage, making it ideal for managing large-scale data workloads.

NEW QUESTION # 66
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?

A. pyspark.sql.types.DateType
B. Cron syntax
C. There is no way to represent and submit this information programmatically
D. pyspark.sql.types.TimestampType
E. datetime

Answer: B

NEW QUESTION # 67
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