DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE VALID EXAM QUESTIONS, DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE LATEST EXAM TESTKING

Databricks-Certified-Data-Engineer-Associate Valid Exam Questions, Databricks-Certified-Data-Engineer-Associate Latest Exam Testking

Databricks-Certified-Data-Engineer-Associate Valid Exam Questions, Databricks-Certified-Data-Engineer-Associate Latest Exam Testking

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100% Pass Quiz 2025 Databricks-Certified-Data-Engineer-Associate: Updated Databricks Certified Data Engineer Associate Exam Valid Exam Questions

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The GAQM Databricks Certified Data Engineer Associate (Databricks-Certified-Data-Engineer-Associate) exam is a certification program designed for professionals who want to validate their expertise in data engineering with Databricks. Databricks is a cloud-based data processing and analytics platform that helps organizations to accelerate innovation and achieve better business outcomes. Databricks-Certified-Data-Engineer-Associate Exam is designed to test the candidate's proficiency in performing various data engineering tasks using Databricks.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

  • A. Option E
  • B. Option D
  • C. Option A
  • D. Option C
  • E. Option B

Answer: A


NEW QUESTION # 55
Which of the following describes the type of workloads that are always compatible with Auto Loader?

  • A. Batch workloads
  • B. Streaming workloads
  • C. Dashboard workloads
  • D. Serverless workloads
  • E. Machine learning workloads

Answer: B

Explanation:
Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables


NEW QUESTION # 56
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?

  • A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
  • B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
  • C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
  • D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
  • E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

Answer: E

Explanation:
1: 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. Reference: 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 # 57
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

  • A. Data Explorer
  • B. Unity Catalog
  • C. Delta Lake
  • D. Auto Loader
  • E. Databricks SQL

Answer: D

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. Reference: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables


NEW QUESTION # 58
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

  • A. They can create a new job from scratch and add both tasks to run concurrently.
  • B. They can clone the existing task in the existing Job and update it to run the new notebook.
  • C. They can create a new task in the existing Job and then add it as a dependency of the original task.
  • D. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
  • E. They can clone the existing task to a new Job and then edit it to run the new notebook.

Answer: C

Explanation:
Explanation
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.


NEW QUESTION # 59
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