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Databricks Certified Professional Data Engineer is an exam designed for professionals who are willing to demonstrate their expertise in building and managing big data pipelines using Databricks. Databricks is a unified analytics platform that provides a collaborative environment for processing large-scale data. The Databricks Certified Professional Data Engineer exam validates the candidate's ability to design, build, and deploy large-scale data processing solutions using Databricks.

Databricks Certified Professional Data Engineer exam covers a wide range of topics, including data engineering concepts, Databricks architecture, data ingestion and processing, data storage and management, and data security. Databricks-Certified-Professional-Data-Engineer exam consists of 60 multiple-choice questions and participants have 90 minutes to complete it. Passing the exam requires a score of 70% or higher, and successful candidates receive a certificate that validates their expertise in building and managing data pipelines on the Databricks platform.

Databricks Certified Professional Data Engineer exam is a certification program designed for data professionals who want to validate their expertise in building and maintaining data pipelines using Databricks. Databricks is a cloud-based data engineering platform that provides a unified analytics engine for big data processing, machine learning, and streaming analytics. Databricks-Certified-Professional-Data-Engineer Exam is designed to test a candidate's ability to design, build, and optimize data pipelines using Databricks, as well as their proficiency in data modeling, data warehousing, and data integration.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
A table in the Lakehouse namedcustomer_churn_paramsis used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
B. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
C. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
D. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
E. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

Answer: D

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.

NEW QUESTION # 91
Review the following error traceback:

Which statement describes the error being raised?

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

Answer: E

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 # 92
Review the following error traceback:

Which statement describes the error being raised?

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

Answer: E

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'. Reference: AnalysisException

NEW QUESTION # 93
Which statement describes the default execution mode for Databricks Auto Loader?

A. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
B. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
C. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
D. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.

Answer: B

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake. The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.
References:
* Databricks Auto Loader Documentation: Auto Loader Guide
* Delta Lake and Auto Loader: Delta Lake Integration

NEW QUESTION # 94
A DELTA LIVE TABLE pipelines can be scheduled to run in two different modes, what are these two different modes?

A. Triggered, Continuous
B. Once, Incremental
C. Triggered, Incremental
D. Continuous, Incremental
E. Once, Continuous

Answer: A

Explanation:
Explanation
The answer is Triggered, Continuous
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/delta-live-tables/delta-live-tables-concepts#-
*Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipeline are updated after their dependent data sources have been updated.
*Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data.

NEW QUESTION # 95
......

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