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Databricks Certified Machine Learning Associate Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
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 limited to a single executor preventing the model from being loaded multiple times
D. The data will be distributed across multiple executors during the inference process

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 # 60
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 more performant than Spark DataFrames
D. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

Answer: B

Explanation:
The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to work with pandas functions on large datasets by leveraging Spark's underlying capabilities.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark

NEW QUESTION # 61
A machine learning engineer has been notified that a new Staging version of a model registered to the MLflow Model Registry has passed all tests. As a result, the machine learning engineer wants to put this model into production by transitioning it to the Production stage in the Model Registry.
From which of the following pages in Databricks Machine Learning can the machine learning engineer accomplish this task?

A. The model version page in the MLflow Model Registry
B. The model page in the MLflow Model Registry
C. The experiment page in the Experiments observatory
D. The home page of the MLflow Model Registry

Answer: A

Explanation:
The machine learning engineer can transition a model version to the Production stage in the Model Registry from the model version page. This page provides detailed information about a specific version of a model, including its metrics, parameters, and current stage. From here, the engineer can perform stage transitions, moving the model from Staging to Production after it has passed all necessary tests.
Reference
Databricks documentation on MLflow Model Registry: https://docs.databricks.com/applications/mlflow/model-registry.html#model-version

NEW QUESTION # 62
A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.
In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

A. When the entire data can fit on each core
B. When the data is particularly long in shape
C. When the tuning process in randomized
D. When the model is unable to be parallelized
E. When the data is particularly wide in shape

Answer: A

Explanation:
Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints. If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.
Reference:
Parallel Computing Concepts

NEW QUESTION # 63
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?

Answer: B

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 # 64
......

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Premium Databricks Databricks-Machine-Learning-Associate Exam & Latest Databricks-Machine-Learning-Associate Study Plan

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q59-Q64):

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Databricks Databricks-Machine-Learning-Associate Exam Questions in Convenient PDF Format