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Databricks Certified Machine Learning Associate Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which of the following statements describes a Spark ML estimator?

A. An estimator is a hyperparameter arid that can be used to train a model
B. An estimator chains multiple alqorithms toqether to specify an ML workflow
C. An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
D. An estimator is an evaluation tool to assess to the quality of a model
E. An estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer

Answer: E

Explanation:
In the context of Spark MLlib, an estimator refers to an algorithm which can be "fit" on a DataFrame to produce a model (referred to as a Transformer), which can then be used to transform one DataFrame into another, typically adding predictions or model scores. This is a fundamental concept in machine learning pipelines in Spark, where the workflow includes fitting estimators to data to produce transformers.
Reference
Spark MLlib Documentation: https://spark.apache.org/docs/latest/ml-pipeline.html#estimators

NEW QUESTION # 64
Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

A. Open the MLmodel artifact in the MLflow run paqe
B. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
C. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page
D. Click the "Source" link in the row corresponding to the run in the MLflow experiment page

Answer: D

Explanation:
To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.
Reference:
MLflow Documentation (Viewing Run Sources and Notebooks).

NEW QUESTION # 65
Which of the following machine learning algorithms typically uses bagging?

A. Random forest
B. Decision tree
C. IGradient boosted trees
D. K-means

Answer: A

Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.
Reference:
Databricks documentation on Random Forest: Random Forest in Spark ML

NEW QUESTION # 66
A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
Which of the following lines of code can the data scientist run to accomplish the task?

A. This task cannot be accomplished in a single line of code.
B. spark_df.summary()
C. spark_df.describe()
D. dbutils.data(spark_df).summarize()
E. dbutils.data.summarize (spark_df)

Answer: E

Explanation:
To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility function dbutils.data.summarize can be used. This function provides a comprehensive summary, including visual histograms.
Correct code:
dbutils.data.summarize(spark_df)
Other options like spark_df.describe() and spark_df.summary() provide textual statistical summaries but do not include visual histograms.
Reference:
Databricks Utilities Documentation

NEW QUESTION # 67
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: D

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 # 68
......

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DATABRICKS-MACHINE-LEARNING-ASSOCIATE RELIABLE TEST DUMPS - DATABRICKS-MACHINE-LEARNING-ASSOCIATE DUMPS DOWNLOAD

Databricks-Machine-Learning-Associate Reliable Test Dumps - Databricks-Machine-Learning-Associate Dumps Download