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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Real Exam Qty:	59
Passing Score:	Not publicly disclosed (approximately 70%)
Exam Format:	Multiple choice, Multi-select
Certificate Validity Period:	2 years
Exam Price:	$200 USD
Available Languages:	English
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Recommended Training:	Advanced Machine Learning Operations Machine Learning at Scale
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

Databricks Certified Machine Learning Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

A. Testing is not possible in real-time serving deployments
B. There is no advantage to using batch serving deployments over real-time serving deployments
C. Batch serving has built-in capabilities in Databricks Machine Learning
D. Querying stored predictions can be faster than computing predictions in real-time
E. Computing predictions in real-time provides more up-to-date results

Answer: C

NEW QUESTION # 152
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which operation can be used to load the model as an sklearn object for batch deployment?

A. mlflow.sklearn.read_model(model_uri)
B. mlflow.pyfunc.load_model(model_uri)
C. mlflow.pyfunc.read_model(model_uri)
D. mlflow.spark.load_model(model_uri)
E. mlflow.sklearn.load_model(model_uri)

Answer: B

NEW QUESTION # 153
A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
In order to do this, they use the following incomplete piece of code:

Which option correctly states:
(i) The complete command to run model training;
(ii) The complete command to execute the prediction on test data;
(iii) The object type of the model object returned by the model
training command.

Answer: A

Explanation:
In Spark MLlib, a Pipeline is trained using the fit method, which applies all estimator stages to the training DataFrame and returns a PipelineModel. Predictions are generated by calling transform on the fitted PipelineModel, which applies the full pipeline (tokenization, feature hashing, and logistic regression) to the test DataFrame.

NEW QUESTION # 154
A machine learning engineer has detected that concept drift is occurring in a production machine learning application. Which result is the impact of concept drift?

A. The model's efficacy will increase
B. The model's latency will increase
C. The model's latency will decrease
D. The model's efficacy will decrease

Answer: D

Explanation:
Concept drift occurs when the relationship between input features and the target variable changes over time. This leads to a decrease in the model's efficacy, as the model is no longer accurately capturing the underlying data patterns it was trained on.

NEW QUESTION # 155
A data scientist is building a model to predict which communication channel (Phone, SMS, Email, or Post) is most likely to be effective for a given customer. Which model type is suited to this task?

A. Softmax Classifier
B. Logistic Regression
C. ARIMA
D. Linear Regression

Answer: A

Explanation:
This task requires predicting one class from multiple mutually exclusive categories. A softmax classifier is designed for multiclass classification problems and outputs a probability distribution across all possible communication channels, allowing selection of the most likely effective channel.

NEW QUESTION # 156
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