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

Topic	Details
Topic 1	Databricks Tooling: The Databricks Tooling topic encompasses the various features and functionalities of Delta Lake. This includes understanding the transaction log, Optimistic Concurrency Control, Delta clone, indexing optimizations, and strategies for partitioning data for optimal performance in the Databricks SQL service.
Topic 2	Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.
Topic 3	Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.

Databricks Certified Professional Data Engineer certification exam is a prestigious certification that validates the skills and expertise of data professionals in building and managing complex data solutions on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows in a collaborative environment. Databricks-Certified-Professional-Data-Engineer Exam validates the ability to design and implement scalable and reliable data solutions using Databricks technologies.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
Which is a key benefit of an end-to-end test?

A. It pinpoint errors in the building blocks of your application.
B. It makes it easier to automate your test suite
C. It closely simulates real world usage of your application.
D. It provides testing coverage for all code paths and branches.

Answer: C

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
:
Software Testing: End-to-End Testing

NEW QUESTION # 171
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. return spark.read.option("readChangeFeed", "true").table ("bronze")
B.
C. return spark.readStream.table("bronze")
D.
E. eturn spark.readStream.load("bronze")

Answer: D

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

NEW QUESTION # 172
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse.
The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
C. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
D. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Answer: B

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

NEW QUESTION # 173
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. All records are cached to an operational database and then the filter is applied
B. The Parquet file footers are scanned for min and max statistics for the latitude column
C. The Hive metastore is scanned for min and max statistics for the latitude column
D. The Delta log is scanned for min and max statistics for the latitude column
E. All records are cached to attached storage and then the filter is applied

Answer: D

Explanation:
Explanation
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.

NEW QUESTION # 174
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
E. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Answer: B

Explanation:
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case, the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.

NEW QUESTION # 175
......

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