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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	DE-A
Related Certifications:	Databricks Certified Data Engineer Professional
Exam Price:	USD 200
Exam Duration:	90 minutes
Certificate Validity Period:	2 years
Exam Format:	Scenario-based questions, Proctored online or test center, Multiple-choice
Passing Score:	Approximately ~70% (not officially fixed/publicly guaranteed)
Available Languages:	English
Real Exam Qty:	45 scored multiple-choice questions
Recommended Training:	Databricks Data Engineering Learning Path (Databricks Academy) Official Data Engineering with Databricks Course
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored exam or test center
Pre Condition:	No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Associate 무료샘플문제 (Q163-Q168):

질문 # 163
What is stored in a Databricks customer's cloud account?

A. Cluster management metadata
B. Data
C. Notebooks
D. Databricks web application

정답：B

설명：
In a Databricks customer's cloud account, the primary elements stored include:
Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.
Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.
Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks.

질문 # 164
A data engineer is transforming a Bronze table containing API-response data into a Silver table. The Bronze table has a user_profile column of type STRING that contains JSON dat a. An example value is:
{"user_id":"12345","name":"John Smith","age":32,"email":"john@example.com"} The Silver table must make this data easily queryable for analytics without requiring JSON parsing in every downstream query.
Which approach standardizes this column for the Silver table?

A. SELECT
get_json_object(user_profile, 'user_id') AS user_id,
get_json_object(user_profile, 'name') AS name,
get_json_object(user_profile, 'age') AS age,
get_json_object(user_profile, 'email') AS email
FROM bronze_table;
B. SELECT
user_profile.user_id AS user_id,
user_profile.name AS name,
CAST(user_profile.age AS INT) AS age,
user_profile.email AS email
FROM bronze_table;
C. SELECT
from_json(
user_profile,
'user_id STRING, name STRING, age STRING, email STRING'
) AS parsed_profile
FROM bronze_table;
D. SELECT
get_json_object(user_profile, '$.user_id') AS user_id,
get_json_object(user_profile, '$.name') AS name,
CAST(get_json_object(user_profile, '$.age') AS INT) AS age,
get_json_object(user_profile, '$.email') AS email
FROM bronze_table;

정답：D

설명：
Option C extracts each JSON field into a separate Silver-table column and casts age to the appropriate integer type. get_json_object accepts a JSON string and a JSONPath expression rooted at $; paths such as $.user_id and $.age therefore identify the required top-level fields correctly. This produces analytics-ready columns and prevents downstream queries from repeatedly parsing the original JSON string. Option A returns one nested struct and incorrectly defines age as STRING, so further projection or casting would still be required. Option B attempts field notation directly on a STRING column, which is not valid until the JSON has been parsed into a structured type. Option D omits the required $ JSONPath root and leaves age uncast. Therefore, option C most completely standardizes the source data for the Silver layer.

질문 # 165
A data engineer is preparing a Declarative Automation Bundle, formerly known as a Databricks Asset Bundle, to deploy a Lakeflow pipeline.
To ensure that the pipeline is deployed to the correct environment, where should workspace-specific configurations, such as the workspace host URL and root storage path, be defined within the bundle project?

A. In a global environment variable on the local development machine
B. In the targets section of the databricks.yml file
C. Within the Python source code of the Lakeflow pipeline
D. Inside the README.md file in the bundle's root directory

정답：B

설명：
Workspace-specific deployment settings should be defined under each environment in the targets section of databricks.yml. A bundle target represents a deployment environment such as development, staging, or production. Each target can contain its own workspace mapping, including the workspace host and an optional root_path. This structure allows one bundle codebase to be deployed predictably to different environments without embedding infrastructure details in notebooks or pipeline source code. A README file is documentation and is not interpreted as bundle configuration. Hardcoding workspace details in Python couples transformation logic to a particular environment. Global local environment variables are machine-specific and do not provide a complete, version-controlled bundle definition. Therefore, option C correctly places the workspace-specific settings in the declarative configuration used during validation and deployment.

질문 # 166
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A. They can set up DLT to notify them via email when records are dropped.
B. They can set up separate expectations for each table when developing their DLT pipeline.
C. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
D. They cannot determine which table is dropping the records.
E. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.

정답：E

설명：
Explanation
To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.

질문 # 167
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

A. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
B. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
C. There is no way to determine why a Job task is running slowly.
D. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.

정답：D

질문 # 168
......

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