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

Section	Objectives
Topic 1: Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control
Topic 2: Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs
Topic 3: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines
Topic 4: Data Modeling and Storage	- Design scalable data lakehouse architectures - Delta Lake table design and optimization - Schema evolution and data partitioning strategies

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

A.
B. return spark.readStream.table("bronze")
C. return spark.read.option("readChangeFeed", "true").table ("bronze")
D. eturn spark.readStream.load("bronze")
E.

Answer: E

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 138
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 thedatevariable:

Assume that the fieldscustomer_idandorder_idserve 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 willonly contain unique records, but newly written records may have duplicates already present in the target table.
B. 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.
C. Each write to the orders table willrun deduplication over the union of new and existing records, ensuringno duplicate records are present.
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 willonly contain unique records, and only those records without duplicates in the target table will be written.

Answer: A

Explanation:
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 usingmergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 139
Which method can be used to determine the total wall-clock time it took to execute a query?

A. Open the Query Profiler associated with that query and use the Aggregated task time metric.
B. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
C. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
D. In the Spark UI, take the job duration of the longest-running job associated with that query.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

NEW QUESTION # 140
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. A user can only transfer job ownership to a group if they are also a member of that group.
B. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C. Other than the default "admins" group, only individual users can be granted privileges on jobs.
D. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E. Only workspace administrators can grant "Owner" privileges to a group.

Answer: D

Explanation:
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 # 141
You are working on IOT data where each device has 5 reading in an array collected in Celsius, you were asked to covert each individual reading from Celsius to Fahrenheit, fill in the blank with an appropriate function that can be used in this scenario.
Schema: deviceId INT, deviceTemp ARRAY<double>

SELECT deviceId, __(deviceTempC,i-> (i * 9/5) + 32) as deviceTempF
FROM sensors

A. TRANSFORM
B. ARRAYEXPR
C. MULTIPLY
D. APPLY
E. FORALL

Answer: A

Explanation:
Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)

NEW QUESTION # 142
......

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