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

Topic	Details
Topic 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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 live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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 the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 6	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature 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 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 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 10	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur

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Databricks Certified Machine Learning Professional Sample Questions (Q47-Q52):

NEW QUESTION # 47
A machine learning engineer needs a python_model to access a collection of files using its load_context operation. The collection of files being accessed by python_model.load_context needs to be saved when the model is being logged. A dictionary of the names and paths of these files is properly stored in my_dict.
The machine learning engineer has written the following incomplete block of code:

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

A. model_context
B. context
C. artifacts
D. None of these lines of code can be used to successfully complete the code block

Answer: C

Explanation:
When logging a custom Python model with mlflow.pyfunc.log_model(), the artifacts parameter is used to specify a dictionary (my_dict) of file names and their paths that the model may need to access during loading or inference. These files are stored as model artifacts and become available to the model through the load_context method.

NEW QUESTION # 48
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. None. Staging. Production
B. Staging. Production. Archived
C. None. Staging. Production. Archived
D. Development. Staging. Production
E. Development. Staging. Production. Archived

Answer: D

NEW QUESTION # 49
A Machine Learning Engineer has automated a model retraining job in Databricks. Each scheduled run trains multiple candidate models with new sales data and logs all runs with MLflow.
The goal is to select and register the best-performing model at the end of each cycle to ensure optimal forecast accuracy. Which approach will meet this goal?

A. Register the best model based on the primary evaluation metric.
B. Register the first model that has an evaluation metric better than the previously deployed model.
C. Register the model with the lowest training loss regardless of validation metrics.
D. Register all models from the run since all of them are trained on newer data and hence will be more accurate.

Answer: A

Explanation:
Selecting and registering the model that performs best on the primary evaluation metric ensures that only the highest-quality model is promoted at each retraining cycle. This approach aligns with MLOps best practices by basing promotion decisions on objective performance criteria rather than training order or assumptions about data freshness.

NEW QUESTION # 50
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark. Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. mlflow.search_runs(exp_id)
B. client.list_run_infos(exp_id)
C. spark.read.format("mlflow-experiment").load(exp_id)
D. spark.read.format("delta").load(exp_id)
E. There is no way to programmatically return row-level results from an MLflow Experiment.

Answer: D

NEW QUESTION # 51
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 object storage
B. Cloud-based in-memory instance of a Spark DataFrame
C. Cloud-based low latency database
D. On-premises low-latency database

Answer: A

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 # 52
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