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Databricks Certified Professional Data Engineer exam is a comprehensive assessment that covers a wide range of topics related to data engineering using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design, build, and optimize data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer exam is available online and can be taken from anywhere in the world, making it a convenient option for data professionals who want to validate their expertise in Databricks. Upon successful completion of the exam, candidates will receive a Databricks Certified Professional Data Engineer certification, which will demonstrate their proficiency in data engineering using Databricks.

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Databricks Certified Professional Data Engineer Certification Exam can be attempted by professionals and students who have experience in data engineering, data management, ETL, and data processing. The preparation for the exam can be done via online training courses such as the Databricks Data Engineering Certification Preparation Course, the online Databricks Documentation, and different study materials such as books and videos from verified training providers.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.format("delta").save("/preds/churn_preds")
B. preds.write.mode("append").saveAsTable("churn_preds")
C.
D.
E.

Answer: B

NEW QUESTION # 43
Review the following error traceback:

Which statement describes the error being raised?

A. There is a syntax error because the heartrate column is not correctly identified as a column.
B. The code executed was PvSoark but was executed in a Scala notebook.
C. There is a type error because a DataFrame object cannot be multiplied.
D. There is no column in the table named heartrateheartrateheartrate
E. There is a type error because a column object cannot be multiplied.

Answer: D

Explanation:
Explanation
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns
'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as
'heartrate'. References: AnalysisException

NEW QUESTION # 44
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Answer: D

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 45
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. Whenever a database is being created, make sure that the location keyword is used
B. Whenever a table is being created, make sure that the location keyword is used.
C. When the workspace is being configured, make sure that external cloud object storage has been mounted.
D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
E. When tables are created, make sure that the external keyword is used in the create table statement.

Answer: B

NEW QUESTION # 46
A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors' group can view the job but not modify/run it.
* Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

A. resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
B. resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
- group_name: admin-team
level: IS_OWNER
C. permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
D. resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW

Answer: A

Explanation:
Comprehensive and Detailed Explanation from Databricks Documentation:
Databricks Asset Bundles (DABs) allow jobs, clusters, and permissions to be defined as code in YAML configuration files. According to the Databricks documentation on job permissions and bundle deployment, when defining permissions within a job resource, they must be scoped directly under that specific job's definition. This ensures that permissions are applied only to the intended job resource and not inadvertently propagated to other jobs or resources.
In this scenario, the data engineer must grant the data-engineers group CAN_MANAGE access, allowing them to configure, edit, and manage the job, while the auditors group should only have CAN_VIEW, giving them read-only access to see configurations and results without the ability to modify or execute. Importantly, no additional groups should be granted permissions, in order to follow the principle of least privilege.
Options A and B introduce unnecessary or unintended groups (like admin-team in A) or define permissions outside of the job scope (as in B). Option C improperly separates the permissions block outside the job resource, which is not aligned with Databricks bundle best practices.
Option D is the correct approach because it defines the job resource my-job with its name, tasks, clusters, and the exact intended permissions (CAN_MANAGE for data-engineers and CAN_VIEW for auditors). This aligns with Databricks' principle of least privilege and ensures compliance with governance standards in Unity Catalog-enabled workspaces.

NEW QUESTION # 47
......

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100% Pass Databricks - Pass-Sure Reliable Databricks-Certified-Professional-Data-Engineer Study Materials

Databricks-Certified-Professional-Data-Engineer Exams Dumps, Databricks-Certified-Professional-Data-Engineer Exam Overview

Databricks Certified Professional Data Engineer Exam Sample Questions (Q42-Q47):

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