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Databricks Certified Professional Data Engineer exam is a hands-on exam that requires the candidate to complete a set of tasks using Databricks. Databricks-Certified-Professional-Data-Engineer exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to optimize and tune data pipelines for performance and reliability.

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 (Databricks-Certified-Professional-Data-Engineer) Exam is a certification program designed for individuals who want to demonstrate their expertise in building, deploying, and maintaining data engineering solutions using Databricks. Databricks-Certified-Professional-Data-Engineer exam is intended for data engineers, data architects, and other data professionals who work with large-scale data processing systems and want to validate their skills and knowledge in this area.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
B. Schedule a job to execute the pipeline once hour on a new job cluster.
C. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D. Configure a job that executes every time new data lands in a given directory.

Answer: B

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
Reference:
Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html

NEW QUESTION # 61
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. Model, predict (df, columns)
B. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
C. Df.apply(model, columns). Select (''customer_id, prediction''
D. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')

Answer: A

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 # 62
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Specify a new checkpointlocation
B. Run REFRESH TABLE delta, /item_agg'
C. Increase the shuffle partitions to account for additional aggregates
D. Remove .option (mergeSchema', true') from the streaming write

Answer: A

Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
Reference:
Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured-streaming/index.html Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured-streaming/production.html#checkpointing

NEW QUESTION # 63
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
B. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
C. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
D. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
E. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

Answer: B

Explanation:
Explanation
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under
"CREATE VIEW" section.

NEW QUESTION # 64
Review the following error traceback:

Which statement describes the error being raised?

A. There is no column in the table named heartrateheartrateheartrate
B. There is a type error because a column object cannot be multiplied.
C. There is a type error because a DataFrame object cannot be multiplied.
D. The code executed was PvSoark but was executed in a Scala notebook.
E. There is a syntax error because the heartrate column is not correctly identified as a column.

Answer: E

Explanation:
Explanation
The error is a Py4JJavaError, which means that an exception was thrown in Java code called by Python code using Py4J. Py4J is a library that enables Python programs to dynamically access Java objects in a Java Virtual Machine (JVM). PySpark uses Py4J to communicate with Spark's JVM-based engine. The error message shows that the exception was thrown by org.apache.spark.sql.AnalysisException, which means that an error occurred during the analysis phase of Spark SQL query processing. The error message also shows that the cause of the exception was "cannot resolve 'heartrateheartrateheartrate' given input columns". This means that Spark could not find a column named heartrateheartrateheartrate in the input DataFrame or Dataset. The reason for this error is that there is a syntax error in the code that caused this exception. The code is:
df.withColumn("heartrate", heartrate * 3)
The code tries to create a new column called heartrate by multiplying an existing column called heartrate by 3.
However, the code does not correctly identify the heartrate column as a column object, but rather as a plain Python variable. This causes PySpark to concatenate the variable name with itself three times, resulting in heartrateheartrateheartrate, which is not a valid column name. To fix this error, the code should use one of the following ways to identify the heartrate column as a column object:
df.withColumn("heartrate", df["heartrate"] * 3) df.withColumn("heartrate", df.heartrate * 3) df.withColumn("heartrate", col("heartrate") * 3) Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Py4J Documentation, under "What is Py4J?"; Databricks Documentation, under "Query plans - Analysis phase"; Databricks Documentation, under "Accessing columns".

NEW QUESTION # 65
......

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Valid Test Databricks-Certified-Professional-Data-Engineer Tips, Databricks-Certified-Professional-Data-Engineer Test Guide

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q60-Q65):

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