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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Associate |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Choice, Multiple Select |
| Passing Score: | 70% |
| Exam Price: | $200 USD |
| Real Exam Qty: | 60 |
| Related Certifications: | Databricks Certified Data Analyst Associate |
| Sample Questions: | Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored or in-person testing center |
| Pre Condition: | Recommended: 6+ months of experience with Databricks and Apache Spark |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-associate |
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Databricks Certified Data Engineer Associate certification is essential for individuals who want to work with big data and build scalable data pipelines. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics, including data modeling, data ingestion, data transformation, data storage, and data processing. Candidates will also be tested on their knowledge of Apache Spark, a popular open-source data processing engine, and Databricks Unified Analytics Platform.
NEW QUESTION # 96
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: D
Explanation:
Explanation
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url "<jdbc_url>",
dbtable "<table_name>",
user '<username>',
password '<password>'
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql
NEW QUESTION # 97
A data engineer needs to read files from cloud object storage into a Spark DataFrame in Databricks. The files are stored in CSV format with headers and comma delimiters. Which Spark DataFrame reader option ensures that column names are correctly inferred from the first row?
Answer: A
NEW QUESTION # 98
A data engineer needs to audit who made changes to a Delta table in the last 7 days. Which SQL statement returns the record of past operations, including timestamps, users, and operation types?
Answer: B
Explanation:
DESCRIBE HISTORY my_table returns the Delta table's operation history, including timestamps, operation types, and user information. The results can then be filtered to the last seven days.
NEW QUESTION # 99
A pipeline uses COPY INTO to ingest CSV files from cloud object storage into a Unity Catalog Delta table. Some files are occasionally re-uploaded with corrections using the same filename.
The engineer needs the corrected data ingested as soon as it occurs. What should the engineer do?
Answer: A
Explanation:
COPY INTO skips files it has already loaded, even when a file is modified and re-uploaded under the same name. Corrections should arrive as new files and then be applied to the target with update logic such as MERGE.
NEW QUESTION # 100
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?
Answer: C
Explanation:
Databricks recommends managed tables when you want the platform to control both the table metadata and the underlying storage lifecycle. For Unity Catalog managed tables, Databricks manages storage layout and applies platform-managed optimization features over time. Databricks also documents that when you drop a managed table , the underlying cloud data is deleted automatically after the managed retention period, which simplifies cleanup and reduces storage administration. That behavior matches the requirement exactly.
External tables, by contrast, are used when you want to retain direct control over the storage path and lifecycle. Databricks explicitly states that dropping an external table removes only the metadata and does not delete the underlying files . Foreign tables are for federation scenarios against external systems, not for Bronze ingestion storage managed by Databricks. Temporary views also do not provide the desired governed lifecycle. Because the team wants Databricks-managed storage, managed retention, and simplified lifecycle handling, managed tables are the correct choice for these Bronze ingestion tables.
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NEW QUESTION # 101
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