DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VALID EXAM NOTES, DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER TRAINING KIT

Databricks-Certified-Professional-Data-Engineer Valid Exam Notes, Databricks-Certified-Professional-Data-Engineer Training Kit

Databricks-Certified-Professional-Data-Engineer Valid Exam Notes, Databricks-Certified-Professional-Data-Engineer Training Kit

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Databricks Certified Professional Data Engineer certification exam is a valuable certification for data professionals who want to demonstrate their skills in building and managing complex data solutions on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows in a collaborative environment. Candidates who pass the exam will have a deep understanding of Databricks technologies and will be able to build and manage scalable and reliable data solutions.

Databricks Certified Professional Data Engineer certification exam is designed to test the knowledge and skills of data engineers who work with Databricks. Databricks is a cloud-based platform that provides a unified analytics engine for big data processing and machine learning. It is used by data engineers to manage data pipelines, extract insights from data, and build machine learning models. Databricks Certified Professional Data Engineer Exam certification exam is a comprehensive assessment of the candidate's ability to use Databricks effectively for data engineering tasks.

Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for professionals who want to demonstrate their expertise in using Databricks to manage big data and create data pipelines. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers, data architects, data scientists, and other professionals who work with big data and want to validate their skills in using Databricks to build data pipelines.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
A new data engineer [email protected] has been assigned to an ELT project. The new data
engineer will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the table to the new data engineer?

Answer: D


NEW QUESTION # 125
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

  • A. A managed table will be created in the DBFS root storage container.
  • B. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
  • C. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
  • D. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
  • E. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

Answer: C

Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html


NEW QUESTION # 126
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

  • A. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
  • B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
  • C. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
  • D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
  • E. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

Answer: C

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.


NEW QUESTION # 127
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer
"Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

  • A. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
  • B. Other than the default "admins" group, only individual users can be granted privileges on jobs.
  • C. Only workspace administrators can grant "Owner" privileges to a group.
  • D. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
  • E. A user can only transfer job ownership to a group if they are also a member of that group.

Answer: D

Explanation:
The reason why the junior data engineer cannot transfer "Owner" privileges to the "DevOps" group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer "Owner" privileges to the "DevOps" group is not possible. References:
* Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html
* Job permissions:
https://docs.databricks.com/security/access-control/table-acls/privileges.html#job-permissions


NEW QUESTION # 128
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

  • A. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at anypoint.
  • B. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
  • C. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
  • D. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.

Answer: A

Explanation:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership:When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema doesnot automaticallyextend to ownership or permission of individual tables within that schema.
* Table Permissions:For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access:The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution:As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership](https://docs.databricks.com/security/access-control
/workspace-acl.html#permissions


NEW QUESTION # 129
......

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