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

Topic
Details

Topic 1

• Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

Topic 2

• Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.

Topic 3

• Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

Topic 4

• ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

 

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".
Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

• A. mlflow.register_model(f"runs:/{run_id}/model", "best_model")
• B. mlflow.register_model(run_id, "best_model")
• C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
• D. millow.register_model(f"runs:/{run_id)/model")

Answer: A

Explanation:
To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:
mlflow.register_model(f"runs:/{run_id}/model", "best_model")
This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.
Reference
MLflow documentation on model registry: https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

 

NEW QUESTION # 35
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

• A. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
• B. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
• C. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
• D. pandas API on Spark DataFrames are unrelated to Spark DataFrames
• E. pandas API on Spark DataFrames are more performant than Spark DataFrames

Answer: B

Explanation:
Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.
Reference
pandas API on Spark documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

 

NEW QUESTION # 36
Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

• A. Grid Search
• B. Tree of Parzen Estimators
• C. Random Search
• D. Halving Random Search

Answer: B

Explanation:
Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next.
This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process.
Reference:
Hyperopt and TPE

 

NEW QUESTION # 37
A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?

• A.
• B.
• C.
• D.

Answer: A

Explanation:
To find the run_id of the run with the best root-mean-square error (RMSE) in an MLflow experiment, the correct line of code to use is:
mlflow.search_runs( experiment_id, order_by=["metrics.rmse"] )["run_id"][0] This line of code searches the runs in the specified experiment, orders them by the RMSE metric in ascending order (the lower the RMSE, the better), and retrieves the run_id of the best-performing run. Option C correctly represents this logic.
Reference
MLflow documentation on tracking experiments: https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.search_runs

 

NEW QUESTION # 38
A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

• A. The model only needs to be loaded once per executor rather than once per batch during the inference process
• B. The model will be limited to a single executor preventing the data from being distributed
• C. The data will be distributed across multiple executors during the inference process
• D. The data will be limited to a single executor preventing the model from being loaded multiple times

Answer: A

Explanation:
Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.
Reference:
Databricks documentation on pandas UDFs: Pandas UDFs

 

NEW QUESTION # 39
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