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

Topic	Details
Topic 1	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 2	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 3	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 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 8	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 9	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
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 (Q22-Q27):

NEW QUESTION # 22
A Machine Learning Engineer has a large dataset with a customer_region column and wants to train separate models for each region, then generate predictions. They need to parallelize this group-specific model training process using Databricks and the Pandas Function API. Which approach will implement this solution?

A. Use mapInPandas() to apply the training function across all partitions without grouping.
B. Use groupBy("customer_region") and apply a training function with applyInPandas().
C. Use foreachBatch() to sequentially process each region's data.
D. Use collect() to gather all data and process regions using standard pandas groupby.

Answer: B

Explanation:
The Pandas Function API supports parallel, group-specific processing by using groupBy on the grouping column and applyInPandas to execute a custom training function independently for each group. This enables separate models to be trained per region in parallel across the cluster, with each function invocation receiving only the data for its group.

NEW QUESTION # 23
A Machine Learning Engineer needs a continuous deployment pipeline for their models hosted on Databricks Model Serving. The deployment automation should execute after a model is trained and registered using MLflow. The goal of the automation is to deploy the latest version of the model from the MLflow Model Registry only if the model can meet the company's strict latency requirements (P95 < 300ms) while serving production traffic. How can the engineer validate that new models meet their latency requirements when served in production?

A. Serve the latest model on Databricks Model Serving and use a load testing client to generate requests at the production request rate. Use the load testing client to calculate P95 latency and verify it is less than 300ms.
B. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the inference_latency metric is less than 300ms.
C. A/B test the latest model with Databricks Model Serving so that the latest model receives 5% of production traffic and the current model receives the rest. Use inference tables to calculate P95 latency and verify it is less than 300ms.
D. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the model_latency metric is less than 300ms.

Answer: C

Explanation:
Validating latency requirements must be done under real production serving conditions. Routing a small percentage of live production traffic to the new model using Databricks Model Serving allows accurate measurement of end-to-end serving latency. Inference tables capture request latency metrics, enabling calculation of the P95 latency and ensuring it meets the strict production threshold before full rollout.

NEW QUESTION # 24
A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

A. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
B. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
C. None of these statements
D. Because the streaming deployment is always on, all types of data must be handled without producing an error
E. All of these statements

Answer: B

NEW QUESTION # 25
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. 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, the write_table() method will provide a low-latency lookup on this data.

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 # 26
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 efficacy will increase
B. The model's latency will increase
C. The model's efficacy will decrease
D. The model's latency will decrease

Answer: C

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 # 27
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