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Databricks Certified Professional Data Engineer certification exam covers a wide range of topics, including data ingestion, data processing, data storage, and data analysis. Candidates are required to demonstrate their ability to design and implement data solutions using Databricks, as well as their understanding of best practices for data engineering. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and takes approximately two hours to complete.

Databricks Certified Professional Data Engineer exam is a comprehensive assessment of a candidate's ability to design, implement, and manage data pipelines on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification exam covers a wide range of topics, including data ingestion, data processing, data transformation, and data storage. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the candidate's knowledge of best practices for building efficient and scalable data pipelines that can handle large volumes of data.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q192-Q197):

NEW QUESTION # 192
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 will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. 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.
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 will only contain unique records, but newly written records may have duplicates already present in the target table.

Answer: E

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

NEW QUESTION # 193
Which of the statements are incorrect when choosing between lakehouse and Datawarehouse?

A. Lakehouse cannot serve low query latency with high reliability for BI workloads, only suitable for batch workloads.
B. Lakehouse uses standard data formats like Parquet.
C. Traditional Data warehouses have storage and compute are coupled.
D. Lakehouse can have special indexes and caching which are optimized for Machine learning
E. Lakehouse can be accessed through various API's including but not limited to Py-thon/R/SQL

Answer: A

Explanation:
Explanation
The answer is Lakehouse cannot serve low query latency with high reliability for BI workloads, only suitable for batch workloads.
Lakehouse can replace traditional warehouses by leveraging storage and compute optimizations like caching to serve them with low query latency with high reliability.
Focus on comparisons between Spark Cache vs Delta Cache.
https://docs.databricks.com/delta/optimizations/delta-cache.html
What Is a Lakehouse? - The Databricks Blog
Graphical user interface, text, application Description automatically generated

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NEW QUESTION # 194
Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?

A. COPY INTO table_name
B. INSERT IF EXISTS table_name
C. INSERT INTO OVERWRITE table_name
D. MERGE INTO table_name
E. UPDATE table_name

Answer: D

Explanation:
Explanation
here is the additional documentation for your review.
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-merge-into.html
1.MERGE INTO target_table_name [target_alias]
2. USING source_table_reference [source_alias]
3. ON merge_condition
4. [ WHEN MATCHED [ AND condition ] THEN matched_action ] [...]
5. [ WHEN NOT MATCHED [ AND condition ] THEN not_matched_action ] [...]
6.
7.matched_action
8. { DELETE |
9. UPDATE SET * |
10. UPDATE SET { column1 = value1 } [, ...] }
11.
12.not_matched_action
13. { INSERT * |
14. INSERT (column1 [, ...] ) VALUES (value1 [, ...])

NEW QUESTION # 195
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.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
C. 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.
D. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Answer: C

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References:
Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

NEW QUESTION # 196
Unity catalog helps you manage the below resources in Databricks at account level

A. Meta Stores and Catalogs
B. ML Models
C. All of the above
D. Dashboards
E. Tables

Answer: C

Explanation:
Explanation
The answer is all of the above.
Unity Catalog is a unified governance solution for all data and AI assets including files, tables, machine learning models, and dashboards in your lakehouse on any cloud.

NEW QUESTION # 197
......

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