Databricks-Certified-Data-Engineer-Associate: Databricks Certified Data Engineer Associate Exam PDF - Testinsides Databricks-Certified-Data-Engineer-Associate actual - Databricks-Certified-Data-Engineer-Associate test dumps

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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.

Databricks Certified Data Engineer Associate Exam is ideal for data engineers, data analysts, data scientists, and other professionals who are interested in building and maintaining data pipelines using Databricks. By achieving this certification, candidates can demonstrate their expertise in working with Databricks and their ability to build and maintain scalable data pipelines. Databricks Certified Data Engineer Associate Exam certification can also help professionals advance their careers and open up new job opportunities in the field of data engineering.

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Preparing for the Databricks-Certified-Data-Engineer-Associate Exam requires a deep understanding of Databricks and its various components. Candidates should have experience with Spark SQL, Spark Streaming, and Spark MLlib, as well as a solid understanding of data modeling, data structures, and algorithms. They should also be familiar with common data storage and processing technologies, such as Hadoop, Kafka, and AWS S3.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could only run the entire program on Sundays.
B. They could redesign the data model to separate the data used in the final query into a new table.
C. They could submit a feature request with Databricks to add this functionality.
D. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
E. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.

Answer: E

Explanation:
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. Reference: PySpark SQL Module, Python datetime Module, Databricks Jobs

NEW QUESTION # 92
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain truthful data than Silver tables.
B. Gold tables are more likely to contain a less refined view of data than Silver tables.
C. Gold tables are more likely to contain valuable data than Silver tables.
D. Gold tables are more likely to contain aggregations than Silver tables.
E. Gold tables are more likely to contain more data than Silver tables.

Answer: D

Explanation:
According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. Reference:
What is the medallion lakehouse architecture?
What is a Medallion Architecture?

NEW QUESTION # 93
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 persist without any processing. The compute resources will persist but go unused.
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 at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
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

NEW QUESTION # 94
A data engineer is developing a small proof of concept in a notebook. When running the entire notebook, cluster usage spikes. The data engineer wants to keep the development experience and get real-time results.
Which cluster meets these requirements?

A. All-Purpose Cluster with autoscaling
B. Job Cluster with autoscaling enabled
C. Job Cluster with Photon enabled and autoscaling
D. All-Purpose Cluster with a large fixed memory size

Answer: A

Explanation:
For interactive notebook development, Databricks distinguishes between all-purpose compute and job compute. All-purpose compute is designed for analysis, notebook development, and interactive workloads, while job compute is intended for automated scheduled or triggered jobs. Because the engineer is actively developing a proof of concept in a notebook and wants real-time feedback, an all-purpose cluster is the right compute type. Adding autoscaling makes it better suited to the observed usage spikes, because the cluster can expand when notebook execution demands more resources and scale down afterward, reducing waste compared with a large fixed-size cluster. That makes option B the best answer. Option A keeps the interactive development model, but the fixed large size is less efficient. Options C and D use job clusters, which are better aligned with production jobs rather than iterative notebook-based development. Databricks documentation consistently positions all-purpose compute for collaborative and interactive development, and autoscaling is a standard mechanism to handle variable workloads more efficiently.
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NEW QUESTION # 95
Which two conditions are applicable for governance in Databricks Unity Catalog? (Choose two.)

A. Both catalog and schema must have a managed location in Unity Catalog provided metastore is not associated with a location
B. You can have more than 1 metastore within a databricks account console but only 1 per region.
C. If metastore is not associated with location, it's mandatory to associate catalog with managed locations
D. You can have multiple catalogs within metastore and 1 catalog can be associated with multiple metastore
E. If catalog is not associated with location, it's mandatory to associate schema with managed locations

Answer: B,C

Explanation:
You can have more than 1 metastore within a Databricks account console but only 1 per region:
This is correct. In Databricks Unity Catalog, you can create multiple metastores, but you can only have one metastore per region within an account.
If metastore is not associated with location, it's mandatory to associate catalog with managed locations: This is also correct. If a metastore does not have a default storage location, then the catalog must be associated with a managed location to ensure that data is stored properly within Unity Catalog.

NEW QUESTION # 96
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