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

Topic	Details
Topic 1	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 2	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 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 5	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

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Databricks Certified Machine Learning Professional Sample Questions (Q192-Q197):

NEW QUESTION # 192
A machine learning engineer has developed a random forest model using scikit-learn and registered the model using MLflow. They now want to deploy that model in parallel. Which of the following operations can they use to create a function they can use to deploy the registered scikit- learn model in parallel?

A. mlflow.pyfunk.spark.udf
B. mlflow.sklearn.spark.udf
C. It is not possible to deploy a scikit-learn model in parallel.
D. mlflow.spark.pandas_udf

Answer: B

Explanation:
The correct operation is mlflow.sklearn.spark_udf, which creates a Spark UDF from a registered scikit-learn model. This allows the model to be applied in parallel across a Spark DataFrame, enabling scalable batch inference using Spark.

NEW QUESTION # 193
A Data Scientist is tasked with developing models to forecast product demand. The company offers 5000 different product types, and the Data Scientist must generate weekly forecasts for each type. They have access to two years of historical purchase data and are given ample project budget.
For their next project, they want to build 5000 separate Random Forest models, one for each product type. They aim to train all the models as quickly as possible with minimal setup.
Which approach meets these requirements?

A. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
B. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.
C. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.
D. Create a Databricks Workflow with 5000 tasks. Each task is configured to accept a product ID as a parameter which will then train a model based on the specified product ID.

Answer: A

Explanation:
The pandas function API with grouped map allows data to be grouped by product type and applies a custom training function independently to each group. This approach enables massive parallelism across the cluster with minimal orchestration or setup, making it well suited for rapidly training thousands of independent models in parallel.

NEW QUESTION # 194
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.sklearn.track_model(sklearn_model, "model")
B. mlflow.spark.track_model(sklearn_model, "model")
C. mlflow.sklearn.load_model("model")
D. mlflow.sklearn.log_model(sklearn_model, "model")
E. mlflow.spark.log_model(sklearn_model, "model")

Answer: B

NEW QUESTION # 195
A machine learning engineer has a machine learning pipeline where predictions are updated annually. The final prediction dataset contains millions of rows, and that dataset is irregularly accessed. Which solution should the machine learning engineer use to maintain cost efficiency?

A. Cloud-based low latency database
B. Cloud-based in-memory instance of a Spark DataFrame
C. On-premises low-latency database
D. Cloud-based object storage

Answer: D

Explanation:
For data that is large in size, infrequently accessed, and updated only periodically, cloud-based object storage (such as AWS S3, Azure Blob Storage, or Google Cloud Storage) is the most cost- efficient option. It offers durable, scalable, and inexpensive storage compared to in-memory or low-latency databases, which are optimized for frequent access and real-time performance rather than long-term, infrequent retrieval.

NEW QUESTION # 196
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace "inference" with the path to the location of the Delta table
B. Replace formatfdelta") with format("stream")
C. Replace spark.read with spark.readStream
D. Replace predict with a stream-friendly prediction function
E. Replace schema(schema) with option("maxFilesPerTriqqer", 1}

Answer: E

NEW QUESTION # 197
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