Databricks - Databricks-Machine-Learning-Professional - Newest Databricks Certified Machine Learning Professional Practice Exams

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Databricks Certified Machine Learning Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function provides no benefits over the built-in library-specific model flavors
B. python_function can be used to deploy models without worrying about which library was used to create the model
C. python_function can be used to store models in an MLmodel file
D. python_function can be used to deploy models in a parallelizable fashion
E. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments

Answer: D

NEW QUESTION # 32
Which MLflow function registers a model to the registry?

A. mlflow.register_model()
B. mlflow.log_param()
C. mlflow.create_model()
D. mlflow.deploy_model()

Answer: A

Explanation:
mlflow.register_model() promotes a model into the Model Registry.

NEW QUESTION # 33
A Machine Learning Engineer needs to digitize millions of historical documents spanning 200+ years with vastly different handwriting styles, fonts, languages, document conditions, and paper types. To do this, the engineer wants to train thousands of specialized OCR models to extract text. The number of documents vary significantly. They need an efficient approach for this parallel model training task. Which approach suits their needs?

A. Use Spark MLlib with custom Spark UDFs to distribute across partitions.
B. Use Scikit-learn's ML library within a Pandas UDF to parallelize training across data partitions.
Use groupby.applyInPandas.
C. Use Spark for data preprocessing and orchestrate parallel model training jobs through Databricks workflows.
D. Use Ray map_in_batches to allocate resources based on the various sizes of models and data volumes.

Answer: D

Explanation:
Ray's map_in_batches API is designed for large-scale, heterogeneous parallel workloads where tasks vary significantly in data size, model complexity, and resource requirements. It enables dynamic resource allocation and efficient scheduling across many specialized OCR training jobs, making it well suited for training thousands of models with uneven document volumes and computational needs.

NEW QUESTION # 34
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, all types of data must be handled without producing an error
B. All of these statements
C. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
D. None of these statements
E. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale

Answer: E

NEW QUESTION # 35
A Data Scientist is training a binary classification model using LogisticRegression in SparkML on a large dataset stored in a Delta table. After fitting the pipeline, they want to evaluate the model's performance using an appropriate metric and scalable method across the distributed test data using the SparkML API. Which model evaluation strategy will suit their needs?

A. Use BinaryClassificationEvaluator with rawPredictionCo1 parameter, but first convert probability predictions to binary predictions using a UDF to ensure consistent evaluation across different Spark versions.
B. Use the BinaryClassificationEvaluator on the transformed test DataFrame and specify the appropriate metric.
C. Convert the DataFrame to RDD and use MulticlassMetrics.confusionMatrix() to calculate precision and recall manually, as this avoids the overhead of DataFrame operations in distributed environments.
D. Use MulticlassClassificationEvaluator with weightedPrecision metric since it provides more comprehensive evaluation than BinaryClassificationEvaluator for binary problems.

Answer: B

Explanation:
BinaryClassificationEvaluator is the Spark ML-native, distributed evaluation API designed specifically for binary classification models. It operates directly on the transformed DataFrame produced by the pipeline and efficiently computes scalable metrics such as areaUnderROC or areaUnderPR without requiring data conversion or custom logic, making it the correct and efficient choice for large, distributed datasets.

NEW QUESTION # 36
......

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