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Databricks-Machine-Learning-Associate Exam Dumps : Databricks Certified Machine Learning Associate Exam

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Databricks Databricks-Machine-Learning-Associate Exam Dumps FAQs

Q. # 1: What is the Databricks-Machine-Learning-Associate Exam?

The Databricks-Machine-Learning-Associate exam assesses an individual's ability to use Databricks to perform basic machine learning tasks. It covers topics like AutoML, Feature Store, and MLflow.

Q. # 2: Who should take the Databricks-Machine-Learning-Associate Exam?

The Databricks-Machine-Learning-Associate exam is ideal for individuals with at least 6 months of experience using Databricks for basic machine learning tasks. Data scientists, analysts, and engineers seeking to demonstrate their proficiency in building and deploying ML models on Databricks will find this certification valuable.

Q. # 3: What topics are included in the Databricks-Machine-Learning-Associate Exam?

The Databricks-Machine-Learning-Associate exam covers Databricks Machine Learning (29%), ML Workflows (29%), Spark ML (33%), and Scaling ML Models (9%).

Q. # 4: How many questions are on the Databricks-Machine-Learning-Associate Exam?

The Databricks-Machine-Learning-Associate exam consists of 45 multiple-choice questions.

Q. # 5: How long is the Databricks-Machine-Learning-Associate Exam?

The Databricks-Machine-Learning-Associate exam has a time limit of 90 minutes.

Q. # 6: What is the passing score for the Databricks-Machine-Learning-Associate Exam?

The passing score for the Databricks-Machine-Learning-Associate exam is 70%.

Q. # 7: What is the differebce between Databricks Databricks-Machine-Learning-Associate and Databricks-Machine-Learning-Professional Exams?

The Databricks Machine Learning Associate and Databricks Machine Learning Professional exams differ primarily in terms of difficulty level, scope, and target audience:

  • Databricks-Machine-Learning-Associate Exam: The Databricks-Machine-Learning-Associate Exam focuses on evaluating an individual's ability to perform basic machine learning tasks using Databricks tools. It covers foundational topics such as Databricks Machine Learning, ML Workflows, Spark ML, and Scaling ML Models.
  • Databricks Machine Learning Professional Exam: The Databricks Machine Learning Professional Exam is designed to assess more advanced machine learning tasks in production environments. It emphasizes Experimentation, Model Lifecycle Management, Model Deployment, and Solution and Data Monitoring.

Q. # 8: What materials are available on CertsTopics for the Databricks-Machine-Learning-Associate Exam?

CertsTopics offers comprehensive Databricks-Machine-Learning-Associate PDFs and a testing engine for exam preparation. Our study materials include Databricks-Machine-Learning-Associate exam dumps, detailed questions and answers, and practice tests to help users succeed with confidence.

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Databricks Certified Machine Learning Associate Exam Questions and Answers

Question 1

A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model bycomparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model.

Which of the following possible explanations for this difference is invalid?

Options:

A.

The second model is much more accurate than the first model

B.

The data scientist failed to exponentiate the predictions in the second model prior tocomputingthe RMSE

C.

The datascientist failed to take the logof the predictions in the first model prior to computingthe RMSE

D.

The first model is much more accurate than the second model

E.

The RMSE is an invalid evaluation metric for regression problems

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Question 2

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?

Options:

A.

spark_df.describe()

B.

dbutils.data(spark_df).summarize()

C.

This task cannot be accomplished in a single line of code.

D.

spark_df.summary()

E.

dbutils.data.summarize (spark_df)

Question 3

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

Options:

A.

Keras

B.

Scikit-learn

C.

PyTorch

D.

Spark ML