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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	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 3	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 6	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q71-Q76):

NEW QUESTION # 71
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 decrease
B. The model's latency will increase
C. The model's efficacy will increase
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 # 72
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 groupBy("customer_region") and apply a training function with applyInPandas().
B. Use foreachBatch() to sequentially process each region's data.
C. Use mapInPandas() to apply the training function across all partitions without grouping.
D. Use collect() to gather all data and process regions using standard pandas groupby.

Answer: A

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 # 73
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

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

Answer: B

NEW QUESTION # 74
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors allow models to be exported as library objects
B. Built-in library-specific flavors are required for model signature use
C. Built-in library-specific flavors allow models to be used with any library/span>
D. Built-in library-specific flavors can only be used for logging models

Answer: A

Explanation:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

NEW QUESTION # 75
A Machine Learning Engineer is training a large-scale gradient boosting model using SparkML on a cluster of machines. The training job fails due to memory overflow on a single executor node after processing several iterations. The cluster resources are limited to executor nodes with 16 CPU cores and 64 GB RAM each. The engineer wants to continue training the model without changing hyperparameters or reducing the dataset size. They know Spark's architecture well and want to take advantage of its benefits. Which approach will allow the Machine Learning Engineer to solve this issue?

A. Increase the number of executor nodes and replicate the entire model on each node, then apply model parallelism to train different parts of the model in parallel.
B. Increase the number of executor nodes and replicate the entire model on each node, then implement data parallelism to train on different mini-batches simultaneously.
C. Increase the number of executor nodes and implement model parallelism by splitting the gradient boosting model across executors so each node trains a part of the model.
D. Increase the number of executor nodes and implement data parallelism by partitioning the dataset across executors so each node trains the model on a subset of data.

Answer: D

Explanation:
Spark ML algorithms, including gradient-boosted trees, are designed around data parallelism. By increasing the number of executor nodes, the dataset can be further partitioned so each executor processes only a subset of the data, reducing per-executor memory pressure while keeping the same model configuration and dataset size. This leverages Spark's distributed architecture without requiring model parallelism or hyperparameter changes.

NEW QUESTION # 76
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