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Databricks Certified Professional Data Engineer Exam is an online exam that can be taken from anywhere in the world. Databricks-Certified-Professional-Data-Engineer exam is timed and consists of multiple-choice questions and hands-on tasks that require the candidate to demonstrate their ability to perform specific tasks using Databricks. Databricks-Certified-Professional-Data-Engineer Exam is designed to be challenging and requires a thorough understanding of Databricks data engineering concepts and techniques.

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Databricks Certified Professional Data Engineer exam is a rigorous and comprehensive assessment of a candidate's skills in designing, building, and maintaining data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam covers a wide range of topics, including data storage and retrieval, data processing, data transformation, and data visualization. Candidates are tested on their ability to design and implement scalable and reliable data architectures, as well as their proficiency in troubleshooting and optimizing data pipelines.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
The data science team has created and logged a production 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, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

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

A. Df.apply(model, columns). Select (''customer_id, prediction''
B. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
C. Model, predict (df, columns)
D. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')

Answer: C

Explanation:
Given the information that the model is registered with MLflow and assuming predict is the method used to apply the model to a set of columns, we use the model.predict() function to apply the model to the DataFrame df using the specified columns. The model.predict() function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictions column. When working with PySpark, this predictions column needs to be selected alongside the customer_id to create a new DataFrame with the schema customer_id LONG, predictions DOUBLE.
Reference:
MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.html#python-function-python PySpark MLlib documentation on model prediction: https://spark.apache.org/docs/latest/ml-pipeline.html#pipeline

NEW QUESTION # 84
Which of the following SQL statements can replace python variables in Databricks SQL code, when the notebook is set in SQL mode?
1.%python
2.table_name = "sales"
3.schema_name = "bronze"
4.
5.%sql
6.SELECT * FROM ____________________

A. SELECT * FROM schema_name.table_name
B. SELECT * FROM {schem_name.table_name}
C. SELECT * FROM f{schema_name.table_name}
D. SELECT * FROM ${schema_name}.${table_name}

Answer: D

Explanation:
Explanation
The answer is, SELECT * FROM ${schema_name}.${table_name}
%python
table_name = "sales"
schema_name = "bronze"
%sql
SELECT * FROM ${schema_name}.${table_name}
${python variable} -> Python variables in Databricks SQL code

NEW QUESTION # 85
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function willautomatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A. Option C
B. Option B
C. Option D
D. Option E
E. Option A

Answer: B

Explanation:
Explanation
Option B correctly fills in the blank to meet the specified requirements. Option B uses the
"cloudFiles.schemaLocation" option, which is required for the schema detection and evolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
Configure schema inference and evolution in Auto Loader:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html
Write streaming data:
https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming-data.html

NEW QUESTION # 86
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex,
Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of
the clusters, you notice that there is significant overlap between the clusters. What should you do?

A. Decrease the number of clusters
B. Remove one of the measures
C. Identify additional measures to add to the analysis
D. Increase the number of clusters

Answer: A

NEW QUESTION # 87
Which of the following data workloads will utilize a gold table as its source?

A. A job that cleans data by removing malformatted records
B. A job that queries aggregated data that already feeds into a dashboard
C. A job that enriches data by parsing its timestamps into a human-readable format
D. A job that ingests raw data from a streaming source into the Lakehouse
E. A job that aggregates cleaned data to create standard summary statistics

Answer: B

Explanation:
Explanation
The answer is, A job that queries aggregated data that already feeds into a dashboard The gold layer is used to store aggregated data, which are typically used for dashboards and reporting.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

NEW QUESTION # 88
......

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