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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q94-Q99):

NEW QUESTION # 94
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. Whenever a database is being created, make sure that the location keyword is used
B. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
C. Whenever a table is being created, make sure that the location keyword is used.
D. When tables are created, make sure that the external keyword is used in the create table statement.
E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer: C

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Create an external table" section.

NEW QUESTION # 95
You are currently working with the second team and both teams are looking to modify the same notebook, you noticed that the second member is copying the notebooks to the personal folder to edit and replace the collaboration notebook, which notebook feature do you recommend to make the process easier to collaborate.

A. Databricks Notebooks support real-time coauthoring on a single notebook
B. Databricks notebooks can be exported into dbc archive files and stored in data lake
C. Databricks notebooks support automatic change tracking and versioning
D. Databricks notebook can be exported as HTML and imported at a later time
E. Databricks notebooks should be copied to a local machine and setup source control lo-cally to version the notebooks

Answer: A

Explanation:
Explanation
Answer is Databricks Notebooks support real-time coauthoring on a single notebook Every change is saved, and a notebook can be changed my multiple users.

NEW QUESTION # 96
Which of the following techniques structured streaming uses to create an end-to-end fault toler-ance?

A. Write ahead logging and water marking
B. Write ahead logging and idempotent sinks
C. Checkpointing and idempotent sinks
D. Stream will failover to available nodes in the cluste
E. Checkpointing and Water marking

Answer: C

Explanation:
Explanation
The answer is Checkpointing and idempotent sinks
How does structured streaming achieves end to end fault tolerance:
*First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
*Next, the streaming sinks are designed to be _idempotent_-that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink.
Taken together, replayable data sources and idempotent sinks allow Structured Streaming to en-sure end-to-end, exactly-once semantics under any failure condition.

NEW QUESTION # 97
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. The email and ltv columns will be returned with the values in user itv.
B. Only the email and ltv columns will be returned; the email column will contain the string "REDACTED" in each row.
C. Three columns will be returned, but one column will be named "redacted" and contain only null values.
D. The email, age. and ltv columns will be returned with the values in user ltv.
E. Only the email and itv columns will be returned; the email column will contain all null values.

Answer: B

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

NEW QUESTION # 98
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
B. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
C. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
D. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
E. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

Answer: A

Explanation:
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.

NEW QUESTION # 99
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