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

Section	Weight	Objectives
Model Development	44%	- Distributed training and hyperparameter tuning - Feature Store and automated feature pipelines - Scalable ML pipelines with SparkML - Advanced MLflow usage
Model Deployment	12%	- Custom model serving - Deployment strategies - Model rollout and version management
ML Ops	44%	- Automated retraining workflows - Environment management with Databricks Asset Bundles - Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies

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Databricks Certified Machine Learning Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
In a Spark ML Pipeline, what is the role of a Transformer?

A. Evaluates models
B. Converts datasets into new datasets
C. Fits data
D. Trains models

Answer: B

Explanation:
A Transformer takes a dataset and transforms it into another dataset.

NEW QUESTION # 60
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. fs.score_batch(model_uri, df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)
C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
D. fs.score_model(model_uri, spark_df)
E. fs.score_batch(model_uri, spark_df)

Answer: E

NEW QUESTION # 61
Which evaluator is used for binary classification?

A. ClusteringEvaluator
B. RegressionEvaluator
C. MulticlassEvaluator
D. BinaryClassificationEvaluator

Answer: D

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.

NEW QUESTION # 62
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data. Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

Answer: D

NEW QUESTION # 63
Which of the following is an obstacle related to streaming machine learning applications?

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

Answer: B

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 # 64
......

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