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Databricks Certified Data Engineer Associate certification exam is ideal for data engineers who work with Databricks on a regular basis and want to enhance their skills and knowledge. Databricks-Certified-Data-Engineer-Associate Exam is also suitable for data engineers who are looking to validate their expertise in Databricks and demonstrate their ability to work with the platform. By earning this certification, data engineers can showcase their proficiency in Databricks and increase their career opportunities in the field of data engineering.
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Databricks Certified Data Engineer Associate certification is widely recognized in the industry and is highly valued by employers. Earning this certification demonstrates to employers that a candidate has the skills and knowledge required to build and maintain data solutions on the Databricks platform. Databricks Certified Data Engineer Associate Exam certification can help professionals advance their careers and increase their earning potential.
Databricks Certified Data Engineer Associate Exam Sample Questions (Q176-Q181):
NEW QUESTION # 176
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?
- A. Database
- B. View
- C. Table
- D. Temporary view
- E. Function
Answer: C
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 # 177
Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?
- A. Data lake
- B. Data lakehouse
- C. All of these
- D. Data warehouse
- E. None of these
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 # 178
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
- A. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- B. O They can set up the dashboard's SQL endpoint to be serverless.
- C. O They can reduce the cluster size of the SQL endpoint.
- D. Q They can turn on the Auto Stop feature for the SQL endpoint.
Answer: D
Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
NEW QUESTION # 179
Which file format is used for storing Delta Lake Table?
- A. Parquet
- B. JSON
- C. Delta
- D. SV
Answer: A
Explanation:
Delta Lake tables use the Parquet format as their underlying storage format. Delta Lake enhances Parquet by adding a transaction log that keeps track of all the operations performed on the table. This allows features like ACID transactions, scalable metadata handling, and schema enforcement, making it an ideal choice for big data processing and management in environments like Databricks.
NEW QUESTION # 180
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. Auto Loader
- C. Databricks SQL
- D. Delta Lake
- E. Unity Catalog
Answer: B
NEW QUESTION # 181
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