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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 Price:	USD 200
Exam Format:	Multiple choice
Passing Score:	70%
Real Exam Qty:	60
Available Languages:	English
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
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	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 8	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

Databricks Certified Machine Learning Professional Sample Questions (Q170-Q175):

NEW QUESTION # 170
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.sklearn.track_model(sklearn_model, "model")
B. mlflow.sklearn.log_model(sklearn_model, "model")
C. mlflow.spark.log_model(sklearn_model, "model")
D. mlflow.spark.track_model(sklearn_model, "model")
E. mlflow.sklearn.load_model("model")

Answer: D

NEW QUESTION # 171
A Data Scientist is tasked with developing models to forecast product demand. The company offers 5000 different product types, and the Data Scientist must generate weekly forecasts for each type. They have access to two years of historical purchase data and are given ample project budget.
For their next project, they want to build 5000 separate Random Forest models, one for each product type. They aim to train all the models as quickly as possible with minimal setup.
Which approach meets these requirements?

A. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.
B. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.
C. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
D. Create a Databricks Workflow with 5000 tasks. Each task is configured to accept a product ID as a parameter which will then train a model based on the specified product ID.

Answer: C

Explanation:
The pandas function API with grouped map allows data to be grouped by product type and applies a custom training function independently to each group. This approach enables massive parallelism across the cluster with minimal orchestration or setup, making it well suited for rapidly training thousands of independent models in parallel.

NEW QUESTION # 172
Which MLflow component is used to log parameters, metrics, and artifacts during model training?

A. MLflow Projects
B. Model Registry
C. MLflow Models
D. MLflow Tracking

Answer: D

Explanation:
MLflow Tracking records experiment runs including:
parameters
metrics
artifacts (models, plots, etc.)

NEW QUESTION # 173
In order to connect an MLflow Model Registry Webhook to a Databricks Job, the Job ID must be provided to the code block used to create the webhook. Which approach can be used to obtain a Databricks Job ID?

A. The Job ID field in the Jobs page in the UI
B. All of these approaches
C. The Job ID field in the Job details section of the specific Job page in the UI
D. The Job ID field returned in a query using the Jobs API

Answer: B

Explanation:
A Databricks Job ID can be obtained in multiple ways - it is displayed directly in the Jobs page, in the Job details section of a specific Job, and can also be retrieved programmatically through the Databricks Jobs API. Any of these methods can be used to supply the Job ID when configuring an MLflow Model Registry Webhook.

NEW QUESTION # 174
Which of the following is an obstacle related to streaming machine learning applications?

A. Out-of-order data
B. None of these
C. All of these
D. End-to-end fault tolerance

Answer: C

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 175
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