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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Processing	28%	- Spark SQL - ETL Pipelines - Data Transformation - Structured Streaming
Topic 2: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Topic 3: Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling
Topic 4: Databricks Lakehouse Platform	24%	- Data Management - Lakehouse Architecture - Unity Catalog - Delta Lake
Topic 5: Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A faulty IoT sensor in a factory reports a temperature of -500, causing the LDP pipeline to fail the expectation, which only allows values between -100 and 200 degrees Celsius. The data engineer would like to further analyze the faulty data to better understand the reason behind this. How should the data engineer resolve the faulty data while ensuring data quality standards are maintained?

A. Ignore the error and simply re-run the pipeline, as Databricks will automatically skip the problematic record on the next run.
B. Remove all expectations form the pipeline to prevent any future failures, regardless of data quality.
C. Change the expectation action from fail to warn so that invalid records are included in the output and the pipeline does not fail.
D. Fix the pipeline code and implement a quarantine logic to isolate the faulty data before re-running the pipeline.

Answer: D

Explanation:
Implementing quarantine logic allows invalid records to be isolated for further analysis while keeping them out of trusted datasets. Fixing the pipeline and re-running it ensures the expectation continues to enforce data quality standards, prevents future failures, and enables root-cause analysis of the faulty sensor data without compromising downstream consumers.

NEW QUESTION # 57
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 named preds with 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.
C.
D. preds.write.mode("append").saveAsTable("churn_preds")
E.

Answer: D

NEW QUESTION # 58
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
B. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
C. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
D. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

Answer: B

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

NEW QUESTION # 59
A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A. df.withColumn ("user_email", F.sha2 ("user_email"))
B. df.withColumn ("user_emai", F.expr("uuid()"))
C. df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
D. df.withColumn ("hashed_email", sha2 ("user_email"))

Answer: A

Explanation:
Hashing the email column replaces the original PII with a deterministic, irreversible value while preserving its role as a unique identifier. This ensures no actual email addresses are copied to the sandbox environment, while still allowing consistent joins or user-level analysis if needed.

NEW QUESTION # 60
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
B. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
C. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
D. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
E. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

Answer: B

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

NEW QUESTION # 61
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