Pass Databricks Machine Learning Associate Exam to Get Databricks Certification

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The Databricks Machine Learning Associate exam is hot, and passing it requires a deep understanding of Databricks solutions. Practicing with Databricks Databricks Machine Learning Associate dumps questions can help you reinforce your knowledge and increase your chances of passing the exam. Databricks Machine Learning Associate dumps are available to help you prepare for the Databricks Machine Learning Associate exam. Using Databricks Machine Learning Associate exam dumps questions is one effective way to supplement your study plan and increase your chances of success on exam day. Test free Machine Learning Associate Databricks Machine Learning Associate exam dumps below.

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1. Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

2. 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?

3. A data scientist has developed a machine learning pipeline with a static input data set using Spark ML, but the pipeline is taking too long to process. They increase the number of workers in the cluster to get the pipeline to run more efficiently. They notice that the number of rows in the training set after reconfiguring the cluster is different from the number of rows in the training set prior to reconfiguring the cluster.

Which of the following approaches will guarantee a reproducible training and test set for each model?

4. A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.

They attempt to run the following code block, but it does not accomplish the desired task:





Which of the following changes can the data scientist make to accomplish the task?

5. Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?

6. A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.

Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

7. A data scientist is using MLflow to track their machine learning experiment. As a part of each of their MLflow runs, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values. All parent and child runs are being manually started with mlflow.start_run.

Which of the following approaches can the data scientist use to accomplish this MLflow run organization?

8. Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

9. A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.

Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

10. In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?


 

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