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The GAQM Databricks-Certified-Data-Engineer-Associate certification is an excellent way for professionals to demonstrate their competence and expertise in working with Databricks. Databricks Certified Data Engineer Associate Exam certification validates your knowledge and skills in designing, building, and maintaining data pipelines with Databricks, which is an essential skill for any organization that is looking to leverage big data for business growth.

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The Databricks Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions and is designed to test your understanding of Databricks concepts, including data engineering, data processing, data storage, and data analysis. To pass the exam, you must score at least 70%. Databricks-Certified-Data-Engineer-Associate exam can be taken online and is available in several languages, including English, Spanish, and Chinese. Upon passing the exam, you will receive a Databricks Certified Data Engineer Associate certification, which is recognized globally as a mark of expertise in big data processing using Databricks.

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.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
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 clone the existing task in the existing Job and update it to run the new notebook.
B. They can clone the existing task to a new Job and then edit 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 job from scratch and add both tasks to run concurrently.
E. They can create a new task in the existing Job and then add the original task as a dependency of the new task.

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 # 75
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The DELETE HISTORY command was run on the table
B. The OPTIMIZE command was nun on the table
C. The HISTORY command was run on the table
D. The TIME TRAVEL command was run on the table
E. The VACUUM command was run on the table

Answer: E

Explanation:
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. Reference: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]

NEW QUESTION # 76
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. Databricks SQL
B. Data Explorer
C. Auto Loader
D. Unity Catalog
E. Delta Lake

Answer: C

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. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 77
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. None of these lines of code are needed to successfully complete the task
B. FROM "path/to/csv"
C. USING DELTA
D. USING CSV
E. FROM CSV

Answer: B

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. Reference: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NEW QUESTION # 78
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: E

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

NEW QUESTION # 79
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