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

Section	Objectives
Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns
Production Pipelines and Orchestration	- Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows
Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations
Delta Lake and Data Management	- Schema evolution and enforcement - Time travel and versioning - Delta Lake transactions and ACID properties
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
The following table consists of items found in user carts within an e-commerce website.
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The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Answer: C

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

NEW QUESTION # 42
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
B. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
C. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
D. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

Answer: A

NEW QUESTION # 43
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Improves the quality of your data
B. Yields faster deployment and execution times
C. Troubleshooting is easier since all steps are isolated and tested individually
D. Ensures that all steps interact correctly to achieve the desired end result
E. Validates a complete use case of your application

Answer: C

Explanation:
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.

NEW QUESTION # 44
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Answer: C

NEW QUESTION # 45
The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
B. model.predict(df, columns)
C. df.apply(model, columns).select("customer_id, predictions")
D. df.select("customer_id", model(*columns).alias("predictions"))
E. df.map(lambda x:model(x[columns])).select("customer_id, predictions")

Answer: D

Explanation:
This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias("predictions") is used to name the output column of the model's predictions. This will result in a DataFrame with the desired schema: "customer_id LONG, predictions DOUBLE".

NEW QUESTION # 46
......

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