Databricks Certified Machine Learning Associate - Databricks-Machine-Learning-Associate FREE EXAM DUMPS QUESTIONS & ANSWERS]

Question 1

A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

A. They can refactor their notebook to utilize the pandas API on Spark. B. They can refactor their notebook to use Spark SQL. C. They can refactor their notebook to use the PySpark DataFrame API. D. They can refactor their notebook to process the data in parallel. E. They can refactor their notebook to use the Scala Dataset API.

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Correct Answer: A Vote an answer

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Question 2

A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

A. import pyspark.pandas as ps
df = ps.DataFrame(spark_df) B. import pyspark.pandas as ps
df = ps.to_pandas(spark_df) C. import pandas as pd
df = pd.DataFrame(spark_df) D. spark_df.to_pandas() E. spark_df.to_sql()

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Correct Answer: A Vote an answer

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Question 3

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Iterative optimization B. Spark ML cannot distribute linear regression training C. Singular value decomposition D. Least-squares method E. Logistic regression

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Correct Answer: A Vote an answer

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Question 4

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

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

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Correct Answer: C Vote an answer

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Question 5

A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?

A. Migrate the Job to a Delta Live Tables pipeline B. Change each Task's setting to use a dedicated cluster C. Run each notebook interactively D. Review the matrix view in the Job's runs

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Correct Answer: D Vote an answer

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Question 6

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

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Correct Answer: A Vote an answer

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Question 7

A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

A. Enable autoscaling clusters B. Implement MLflow Experiment Tracking C. Scale up with Spark ML D. Parallelize with Hyperopt

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Correct Answer: D Vote an answer

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Question 8

A data scientist has produced two models for a single machine learning problem. One of the models performs well when one of the features has a value of less than 5, and the other model performs well when the value of that feature is greater than or equal to 5. The data scientist decides to combine the two models into a single machine learning solution.
Which of the following terms is used to describe this combination of models?

A. Stacking B. Ensemble learning C. Bootstrap aggregation D. Bucketing E. Support vector machines

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Correct Answer: B Vote an answer

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