RELIABLE DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM PREP, DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER TEST QUESTIONS VCE

Reliable Databricks-Certified-Professional-Data-Engineer Exam Prep, Databricks-Certified-Professional-Data-Engineer Test Questions Vce

Reliable Databricks-Certified-Professional-Data-Engineer Exam Prep, Databricks-Certified-Professional-Data-Engineer Test Questions Vce

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Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's knowledge of Databricks architecture, data engineering concepts, data processing with Databricks, and data storage with Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to design, implement, and maintain data engineering solutions using Databricks.

Databricks-Certified-Professional-Data-Engineer certification is highly sought after by employers as it provides assurance that the candidate has the necessary skills and knowledge to work with Databricks effectively. Databricks Certified Professional Data Engineer Exam certification is recognized as a standard of excellence in the data engineering field and is a valuable asset for professionals looking to advance their careers.

Databricks Certified Professional Data Engineer Exam is a comprehensive exam that covers a wide range of topics related to data engineering. It includes questions on data ingestion, data transformation, data storage, data processing, and data management using Databricks. Databricks-Certified-Professional-Data-Engineer Exam also covers topics such as cluster management, security, and performance optimization. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's ability to design, implement, and manage data engineering solutions using Databricks.

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Databricks-Certified-Professional-Data-Engineer Test Questions Vce | Reliable Databricks-Certified-Professional-Data-Engineer Exam Dumps

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q102-Q107):

NEW QUESTION # 102
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

  • A. The total count of records is calculated from the parquet file metadata
  • B. The total count of rows is calculated by scanning all data files
  • C. The total count of records is calculated from the Delta transaction logs
  • D. The total count of records is calculated from the Hive metastore
  • E. The total count of rows will be returned from cached results unless REFRESH is run

Answer: C

Explanation:
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%20number,a


NEW QUESTION # 103
How VACCUM and OPTIMIZE commands can be used to manage the DELTA lake?

  • A. VACCUM command can be used to compact small parquet files, and the OP-TIMZE command can be used to delete parquet files that are marked for dele-tion/unused.
  • B. VACCUM command can be used to delete empty/blank parquet files in a delta table. OPTIMIZE command can be used to update stale statistics on a delta table.
  • C. VACCUM command can be used to compress the parquet files to reduce the size of the table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
  • D. VACCUM command can be used to delete empty/blank parquet files in a delta table, OPTIMIZE command can be used to cache frequently delta tables for better perfor-mance.
  • E. OPTIMIZE command can be used to compact small parquet files, and the VAC-CUM command can be used to delete parquet files that are marked for deletion/unused.
    (Correct)

Answer: E

Explanation:
Explanation
VACCUM:
You can remove files no longer referenced by a Delta table and are older than the retention thresh-old by running the vacuum command on the table. vacuum is not triggered automatically. The de-fault retention threshold for the files is 7 days. To change this behavior, see Configure data reten-tion for time travel.
OPTIMIZE:
Using OPTIMIZE you can compact data files on Delta Lake, this can improve the speed of read queries on the table. Too many small files can significantly degrade the performance of the query.


NEW QUESTION # 104
You have noticed the Data scientist team is using the notebook versioning feature with git integra-tion, you have recommended them to switch to using Databricks Repos, which of the below reasons could be the reason the why the team needs to switch to Databricks Repos.

  • A. Databricks Repos allow you to add comments and select the changes you want to commit.
  • B. Databricks Repos has a built-in version control system
  • C. Databricks Repos allows merge and conflict resolution
  • D. Databricks Repos automatically saves changes
  • E. Databricks Repos allows multiple users to make changes

Answer: A

Explanation:
Explanation
The answer is Databricks Repos allow you to add comments and select the changes you want to commit.


NEW QUESTION # 105
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. Select (''customer_id''.
    Model (''columns) alias (''predictions'')
  • B. Model, predict (df, columns)
  • C. Df.apply(model, columns). Select (''customer_id, prediction''
  • D. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

Answer: B

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 # 106
Which of the following tool provides Data Access control, Access Audit, Data Lineage, and Data discovery?

  • A. DELTA lake
  • B. Lakehouse
  • C. DELTA LIVE Pipelines
  • D. Unity Catalog
  • E. Data Governance

Answer: D


NEW QUESTION # 107
......

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