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

Topic	Details
Topic 1	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
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	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 5	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 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks Certified Machine Learning Professional Sample Questions (Q157-Q162):

NEW QUESTION # 157
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A. mlflow.log_artifact(importance_path, "feature-importance.csv")
B. None of these code blocks tan accomplish the task.
C.
D. mlflow.log_data(importance_path, "feature-importance.csv")
E.

Answer: E

NEW QUESTION # 158
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. mlflow.search_runs(exp_id)
D. There is no way to programmatically return row-level results from an MLflow Experiment.
E. spark.read.format("mlflow-experiment").load(exp_id)

Answer: A

NEW QUESTION # 159
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. 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.
B. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
C. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.
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 # 160
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

A. Real-time
B. Streaming
C. Edge/on-device
D. Batch

Answer: A

Explanation:
Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.

NEW QUESTION # 161
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?

A. Real-time
B. Streaming
C. Edge/on-device
D. None of these strategies will meet the requirements.
E. Batch

Answer: A

NEW QUESTION # 162
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