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

Topic	Details
Topic 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 4	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	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 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 8	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9	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 10	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 11	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 (Q93-Q98):

NEW QUESTION # 93
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

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

Answer: C

NEW QUESTION # 94
A machine learning engineering team has decided that they need to have predictions be made available for querying in continuous, equal-sized increments. A computation can be included in one of the increments when all of its feature values are in the inference Spark DataFrame. Which of the following tools can be used to provide this type of continuous inference?

A. Structured Streaming
B. MLflow
C. Spark UDFs
D. Delta Lake

Answer: A

Explanation:
Structured Streaming in Apache Spark enables continuous inference by processing incoming data in microbatches or continuous increments. It ensures that each computation occurs only when all required feature values are available in the inference DataFrame, supporting real-time or near-real-time prediction pipelines with consistent, equal-sized processing intervals.

NEW QUESTION # 95
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, spark_df)
B. fs.score_model(model_uri, spark_df)
C. fs.score_batch(model_uri, df)
D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
E. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
df = fs.get_missing_features(spark_df)

Answer: A

NEW QUESTION # 96
Which MLflow function logs files like plots or datasets?

A. mlflow.log_metric()
B. mlflow.log_artifact()
C. mlflow.save_model()
D. mlflow.log_param()

Answer: B

Explanation:
Artifacts include:
charts
model files
datasets.

NEW QUESTION # 97
A machine learning engineer has detected that concept drift is occurring in a production machine learning application. Which result is the impact of concept drift?

A. The model's latency will increase
B. The model's efficacy will increase
C. The model's latency will decrease
D. The model's efficacy will decrease

Answer: D

Explanation:
Concept drift occurs when the relationship between input features and the target variable changes over time. This leads to a decrease in the model's efficacy, as the model is no longer accurately capturing the underlying data patterns it was trained on.

NEW QUESTION # 98
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