Databricks-Machine-Learning-Professional Certification Torrent - Databricks-Machine-Learning-Professional Exam Sims

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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Exam Format:	Multiple choice
Passing Score:	70%
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Real Exam Qty:	60
Exam Price:	USD 200
Certificate Validity Period:	2 years
Available Languages:	English
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 7	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 8	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table

Databricks Certified Machine Learning Professional Sample Questions (Q90-Q95):

NEW QUESTION # 90
A Machine Learning Engineer has deployed a customer churn prediction model to production three months ago. The model serves real-time predictions via a Databricks endpoint with inference logging enabled. They notice declining model accuracy in recent weeks and suspect data drift in customer demographics. They need to implement monitoring to track model performance degradation and input feature drift over time. Which monitoring profile type should they use?

A. Time series profile - Monitor the inference table using timestamp-based windows to track model performance and input drift over time.
B. Inference profile - Monitor the inference table to track model performance metrics, prediction drift, and input feature drift across time windows.
C. Snapshot profile - Monitor the complete inference table to compare current model performance against the baseline established at deployment.
D. Custom profile - Create a hybrid monitoring approach combining snapshot and time series profiles to capture both baseline comparisons and temporal trends.

Answer: B

Explanation:
The inference profile is designed specifically to monitor production model behavior using inference logs. It tracks model performance metrics, prediction distributions, and input feature drift across time windows, enabling detection of performance degradation and demographic data drift after deployment.

NEW QUESTION # 91
A machine learning engineer is in the process of implementing a feature drift monitoring solution.
They are planning to use the following steps:
1. Measure the distributions of each feature variable in the training
set
2. Deploy a model to production
3. Measure the distributions of each feature variable in inference
4. _______
Which action should be completed as Step #4?

A. Obtain the observed values (actual) feature values and compare to the predicted values
B. Retrain the model based on any new feature variables that have been added to the feature set
C. Run a statistical test to determine if there are changes in the feature variable distribution over time
D. Measure the latency of the model's prediction time

Answer: C

Explanation:
The final step in a feature drift monitoring solution is to run a statistical test (e.g., Kolmogorov- Smirnov test) to determine whether the feature distributions in production have significantly diverged from those in the training set. This helps detect drift and maintain model reliability.

NEW QUESTION # 92
A Machine Learning Engineer has deployed a fraud detection model in Databricks Model Serving to detect fraudulent transactions. The engineer wants to compare the model's predictions with the actual fraud classifications from the Fraud Ops team to monitor model performance. The Fraud Ops team uses a unique transaction_id to investigate fraudulent activity and persist their findings to a fraud_findings table. The engineer enabled inference tables on the endpoint, but they are not sure how to map the models' predictions to the Fraud Ops team's classifications. How can the engineer uniquely join the models' prediction to the fraud_findings table with the fewest code changes?

A. Store databricks_request_id returned from each model serving request and persist it to the fraud_findings table. Join the inference table with the fraud_findings table using databricks_request_id as the join key.
B. Populate the client_request_id field with the transaction_id in the model serving request body.
Join the inference table with the fraud_findings table using client_request_id (which contains the transaction_id) as the join key.
C. Modify the model to include an additional input: transaction_id. Log, register and deploy the new model. In the model serving request body, add transaction_id as an additional input feature. Join the inference table with the fraud_findings table using transaction_id as the join key.
D. Join the inference table with the fraud_findings table using timestamp_ms as the join key.

Answer: B

Explanation:
Databricks Model Serving inference tables automatically log the client_request_id field for each request. By populating this field with the existing transaction_id in the request body, the engineer can directly and uniquely join inference predictions with the fraud_findings table using the same identifier, achieving accurate performance monitoring with minimal code changes and no model retraining or redeployment.

NEW QUESTION # 93
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A. features_df.write.mode("feature").path("new_table")
B.
C.
D. features_df.write.mode("fs").path("new_table")
E.

Answer: B

NEW QUESTION # 94
A data scientist wants to examine the data in the Feature Store table table from the database dev as a Spark DataFrame. They have access to Feature Store Client fs. Which line of code can be used to gel the data from table as a Spark DataFrame?

A. fs.create_table("dev.table")
B. fs.get_table("dev.table")
C. fs.get_table("table)
D. fs.read_table("dev.table")

Answer: D

Explanation:
To read data from a Feature Store table as a Spark DataFrame, the correct method is fs.read_table("dev.table"). This retrieves the contents of the table in the dev database using the Feature Store Client (fs) and returns it as a Spark DataFrame.

NEW QUESTION # 95
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