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

Topic	Details
Topic 1	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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 less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 8	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 (Q94-Q99):

NEW QUESTION # 94
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which statement is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. The same preprocessing logic will automatically be applied when calling fit
B. The same preprocessing logic will automatically be applied when calling predict
C. The pvfunc model can be used to deploy models in a parallelizable fashion
D. This approach has no impact when loading the logged Pvfunc model for downstream deployment
E. There is no longer a need for pipeline-like machine learning objects

Answer: E

NEW QUESTION # 95
A Machine Learning Engineer needs to build a time series model. In Databricks, they have created isolated environments in different workspaces for development, staging, and production.
To manage this model, they are planning on using a "deploy code" strategy. They are concerned that the model trained in development will not remain consistent across environments, due to differences in the dependencies installed. What can they do to ensure that the model is trained with the same packages?

A. Use MLflow to track the model parameters and metrics in development, then export the same training conditions in staging and production.
B. Use the same Databricks Runtime (DBR) version across environments and define dependencies in a lock file that is installed as part of the job.
C. Use Databricks Repos to store the model code and install the required libraries in the notebook.
D. Use custom init scripts that install the necessary packages as part of the job.

Answer: B

Explanation:
Using the same Databricks Runtime across environments ensures a consistent base environment, and installing dependencies from a lock file guarantees identical package versions during training. This combination follows best practices for deploy-code workflows by making the training environment reproducible and preventing dependency drift between development, staging, and production.

NEW QUESTION # 96
Which of the following is an obstacle related to streaming machine learning applications?

A. None of these
B. Out-of-order data
C. All of these
D. End-to-end fault tolerance

Answer: C

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 97
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 and publish the table as an online table with the streaming option set to True.
B. 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.
C. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
D. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.

Answer: A

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 # 98
Which evaluator is used for binary classification?

A. MulticlassEvaluator
B. BinaryClassificationEvaluator
C. ClusteringEvaluator
D. RegressionEvaluator

Answer: B

Explanation:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.

NEW QUESTION # 99
......

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