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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Exam Number: | DE-A |
| Exam Price: | USD 200 |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 45 scored multiple-choice questions |
| Passing Score: | Approximately ~70% (not officially fixed/publicly guaranteed) |
| Related Certifications: | Databricks Certified Data Engineer Professional |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice, Proctored online or test center, Scenario-based 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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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is a credential that certifies individuals in their ability to design, develop, and manage data pipelines using Databricks. It is a globally recognized certification that validates the skills and knowledge required to work with big data processing and analytics using Databricks.
NEW QUESTION # 89
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?
Answer: A
Explanation:
A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.
catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. References:
* Create a table
* Create a table from a query result
* Register a table from a DataFrame
* [Query a table]
* [Update, delete, or merge into a table]
NEW QUESTION # 90
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?
Answer: C
Explanation:
In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. References: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks' Delta Live Tables
NEW QUESTION # 91
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:
After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?
Answer: D
Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. Reference: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS
NEW QUESTION # 92
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?
Answer: C
Explanation:
In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. Reference: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?
NEW QUESTION # 93
What is the primary function of the Silver layer in the Databricks medallion architecture?
Answer: A
Explanation:
The Silver layer in the Databricks Medallion Architecture is responsible for validating, cleaning, and deduplicating raw Bronze data. This prepares data in a structured and reliable form, making it ready for downstream enrichment and analytics in the Gold layer.
NEW QUESTION # 94
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