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

TopicDetails
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
  • Describe concept drift and its impact on model efficacy
  • Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3
  • Identify the requirements for tracking nested runs
  • Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry
Topic 5
  • 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 6
  • Describe model serving deploys and endpoint for every stage
  • Identify scenarios in which feature drift and
  • or label drift are likely to occur
Topic 7
  • 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 8
  • 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 9
  • Identify less performant data storage as a solution for other use cases
  • Describe why complex business logic must be handled in streaming deployments
Topic 10
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 11
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow

Databricks Certified Machine Learning Professional Sample Questions (Q39-Q44):

NEW QUESTION # 39
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

Answer: D


NEW QUESTION # 40
A machine learning engineer is working on a fraud detection machine learning application. When a transaction is made with a credit card, the machine learning application will immediately process the data and make a prediction to determine whether or not to approve the transaction based on the probability that the transaction is fraudulent. Which deployment strategy can be used to meet these requirements?

Answer: B

Explanation:
Real-time deployment is required for applications that must provide immediate predictions, such as fraud detection during a credit card transaction. This ensures that the system can instantly assess risk and approve or deny the transaction without delay.


NEW QUESTION # 41
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?

Answer: B

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 # 42
A Data Scientist is using Spark ML to train a model for detecting fraudulent transactions (1:
fraudulent, 0: non-fraudulent). To maximize the model's overall AUC (ROC) score, the Data Scientist wants to perform hyperparameter tuning on a Random Forest classifier. Due to budget constraints, the tuning process must be completed efficiently to avoid excessive compute costs.
Which approach will fulfill their needs?

Answer: A

Explanation:
TrainValidationSplit evaluates each hyperparameter combination using a single train/validation split, which is significantly more compute-efficient than k-fold cross-validation. Using a BinaryClassificationEvaluator aligns directly with optimizing AUC (ROC) for a binary fraud detection task, making this approach well suited for maximizing model performance under budget and compute constraints.


NEW QUESTION # 43
A Machine Learning Engineer is working with a Spark DataFrame containing 100 million rows of retail transactions across thousands of stores. Each store requires its own demand forecasting model using the same scikit-learn pipeline. They want to train and apply these models in parallel for each store without collecting the data to the driver. Which approach will do this?

Answer: B

Explanation:
groupBy().applyInPandas() enables grouped Pandas UDFs that receive each store's data as a Pandas DataFrame on the executors. This allows training and applying a separate scikit-learn model per store in parallel without collecting data to the driver, making it the correct and scalable approach for large Spark DataFrames.


NEW QUESTION # 44
......

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