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Databricks-Certified-Data-Engineer-Associate Preparation Materials and Study Guide: Databricks Certified Data Engineer Associate Exam - Dumpkiller

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Databricks Certified Data Engineer Associate exam is a comprehensive and rigorous assessment of a data engineer's proficiency in Databricks. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including Databricks architecture, data ingestion, data processing, data engineering, data storage, and data management. It is designed to test the candidate's knowledge of Databricks best practices and their ability to implement these practices in real-world scenarios.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q177-Q182):

NEW QUESTION # 177
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 a databricks.yml in a .databricks/ hidden folder at the repository root; only hidden locations are valid for bundle configs.
B. Place a databricks.yml at the repository root and optional databricks.yml in subfolders; the CLI prefers .yaml over .yml when both exist.
C. 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.
D. Place multiple databricks.yml files under each subfolder (for example, jobs/, pipelines/, workspace/) and merge them at deploy time using the include mapping.

Answer: C

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 # 178
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which command can be used to grant full permissions on the database to the new data engineering team?

A. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
B. GRANT ALL PRIVILEGES ON TABLE team TO sales;
C. GRANT SELECT ON TABLE sales TO team;
D. grant all privileges on table sales TO team;

Answer: D

Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.
Reference:
Databricks documentation on SQL permissions: SQL Permissions in Databricks

NEW QUESTION # 179
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. Auto Loader only works with string data
B. All of the fields had at least one null value
C. JSON data is a text-based format
D. Auto Loader cannot infer the schema of ingested data
E. There was a type mismatch between the specific schema and the inferred schema

Answer: C

Explanation:
JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type.
However, Auto Loader also provides an option to infer more precise column types based on the sample data.
This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures.
When set to false, Auto Loader infers all columns as strings. The default value of this option is false. References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

NEW QUESTION # 180
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?

A. Use Lakehouse Federation to query both data sources directly
B. Manually synchronize data from both sources into a single database
C. Develop custom ETL pipelines to ingest data into Databricks
D. Export data from both sources to CSV files and upload them to Databricks

Answer: A

NEW QUESTION # 181
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

A. IGNORE
B. DROP
C. APPEND
D. INSERT
E. MERGE

Answer: E

NEW QUESTION # 182
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