Databricks Databricks-Machine-Learning-Professional Reliable Test Objectives Exam 100% Pass | Databricks-Machine-Learning-Professional: Databricks Certified Machine Learning Professional

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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	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 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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
Topic 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	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

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Databricks Certified Machine Learning Professional Sample Questions (Q114-Q119):

NEW QUESTION # 114
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.spark.track_model(sklearn_model, "model")
C. mlflow.spark.log_model(sklearn_model, "model")
D. mlflow.sklearn.load_model("model")
E. mlflow.sklearn.log_model(sklearn_model, "model")

Answer: B

NEW QUESTION # 115
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(model_uri, "model")
B. mlflow.register_model(f"runs:/{run_id}/model")
C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
D. mlflow.register_model(model_uri, "best_model")
E. mlflow.register_model(run_id, "best_model")

Answer: D

NEW QUESTION # 116
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage. Which code block can they use to accomplish the task?

Answer: D

NEW QUESTION # 117
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Remove the nested=True argument from the child runs
B. Add the nested=True argument to the parent run
C. Provide the same name to the run name parameter for all three run blocks
D. Indent the child run blocks within the parent run block
E. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

Answer: E

NEW QUESTION # 118
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("fs").path("new_table")
B. features_df.write.mode("feature").path("new_table")
C.
D.
E.

Answer: E

NEW QUESTION # 119
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