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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive examination that tests the skills of professionals who work with data on the Databricks platform. Databricks Certified Data Engineer Associate Exam certification is designed to help professionals stay up-to-date with the latest data engineering trends and technologies, and it can help professionals advance their career in the field of data engineering. By earning this certification, professionals can demonstrate their commitment to professional development and their dedication to staying current in their field.
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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.
The GAQM Databricks-Certified-Data-Engineer-Associate Exam is an essential certification for data engineers looking to demonstrate their expertise in working with the Databricks platform. It is recognized globally and is a highly versatile qualification that can be applied to a range of different roles and industries. With the right preparation and training, candidates can pass the exam and take their careers to the next level in the field of big data and analytics.
NEW QUESTION # 19
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: 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 # 20
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT.
Which code snippet should the data engineer use?
Answer: B
NEW QUESTION # 21
A data engineer must support self-serve BI dashboards for hundreds of business users who run ad hoc, high-concurrency SQL queries throughout the day against governed Unity Catalog tables.
The team needs a near-instant start, autoscaling without manual tuning, and the best performance for SQL while minimizing operational overhead. Which Databricks compute should the data engineer use?
Answer: C
Explanation:
Serverless SQL Warehouses provide instant startup, automatic scaling, and optimized performance with Photon for high-concurrency BI workloads, while minimizing operational overhead and management effort.
NEW QUESTION # 22
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
Answer: A
Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.
NEW QUESTION # 23
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?
Answer: D
Explanation:
A temporary view is a relational object that is defined in the metastore and points to an existing DataFrame. It does not copy or store any physical data, but only saves the query that defines the view. The lifetime of a temporary view is tied to the SparkSession that was used to create it, so it does not persist across different sessions or applications. A temporary view is useful for accessing the same data multiple times within the same notebook or session, without incurring additional storage costs. The other options are either materialized (A, E), persistent (B, C), or not relational objects . Reference: Databricks Documentation - Temporary View, Databricks Community - How do temp views actually work?, Databricks Community - What's the difference between a Global view and a Temp view?, Big Data Programmers - Temporary View in Databricks.
NEW QUESTION # 24
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