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

Section	Weight	Objectives
Topic 1: ML Ops	44%	- Testing and validation strategies - Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles - Automated retraining workflows
Topic 2: Model Deployment	12%	- Deployment strategies - Custom model serving - Model rollout and version management
Topic 3: Model Development	44%	- Scalable ML pipelines with SparkML - Advanced MLflow usage - Distributed training and hyperparameter tuning - Feature Store and automated feature pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

A. mlflow.log_figure
B. mlflow.shap.log_explanation
C. None of these operations can accomplish the task.
D. mlflow.shap
E. client.log_artifact

Answer: D

NEW QUESTION # 61
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace POST with PUT in the call to http request
B. Replace list with webhooks in the endpoint URL
C. There are no necessary changes
D. Replace POST with GET in the call to http request
E. Replace list with view in the endpoint URL

Answer: B

NEW QUESTION # 62
A Machine Learning Engineer has previously built a feature table for model training and inference using a batch mode approach:

They have been informed that they now require these features to be available in "real-time", with latency on the order of a minute. Their manager has informed them there is now a Kafka stream from which they can stream live data, and they need to have this ingested and available for low- latency feature lookups.
Which change to their existing code will achieve this?

A. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.
B. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
C. Change the incoming_df to be a dataframe based on a readStream() from the kafka source, the write_table() method will provide a low-latency lookup on this data.
D. Change the incoming_df to be a dataframe based on a readStream() from the Kafka source and publish the table as an online table with the streaming option set to True.

Answer: D

Explanation:
To achieve real-time availability with minute-level latency, the feature data must be continuously ingested from Kafka and published to an online table. Using a streaming DataFrame created with readStream from the Kafka source and enabling the online table with streaming allows incremental updates to be synchronized to the online store, supporting low-latency feature lookups for real-time inference.

NEW QUESTION # 63
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. spark.read.format("delta").load(exp_id)
B. client.list_run_infos(exp_id)
C. There is no way to programmatically return row-level results from an MLflow Experiment.
D. mlflow.search_runs(exp_id)
E. spark.read.format("mlflow-experiment").load(exp_id)

Answer: A

NEW QUESTION # 64
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. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
B. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
C. fs.score_model(model_uri, spark_df)
D. fs.score_batch(model_uri, spark_df)
E. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)

Answer: D

NEW QUESTION # 65
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