Free Databricks Certified Machine Learning Associate Exam Databricks-Machine-Learning-Associate Exam Practice Test

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

• Open the MLmodel artifact in the MLflow run paqe
• Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
• Click the "Source" link in the row corresponding to the run in the MLflow experiment page
• Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page

A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.Which of the following classification metrics should be used to evaluate the model?

• RMSE
• Precision
• Area under the residual operating curve
• Accuracy
• Recall

A data scientist has produced three new models for a single machine learning problem. In the past, the solution used just one model. All four models have nearly the same prediction latency, but a machine learning engineer suggests that the new solution will be less time efficient during inference. In which situation will the machine learning engineer be correct?

• When the new solution requires if-else logic determining which model to use to compute each prediction
• When the new solution's models have an average latency that is larger than the size of the original model
• When the new solution requires the use of fewer feature variables than the original model
• When the new solution requires that each model computes a prediction for every record
• When the new solution's models have an average size that is larger than the size of the original model

A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.Which of the following feature engineering tasks will be the least efficient to distribute?

• One-hot encoding categorical features
• Target encoding categorical features
• Imputing missing feature values with the mean
• Imputing missing feature values with the true median
• Creating binary indicator features for missing values

Which of the following machine learning algorithms typically uses bagging?

• IGradient boosted trees
• K-means
• Random forest
• Decision tree

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?

• Run each notebook interactively
• Review the matrix view in the Job's runs
• Migrate the Job to a Delta Live Tables pipeline
• Change each Task’s setting to use a dedicated cluster

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?

• import pyspark.pandas as ps df = ps.DataFrame(spark_df)
• import pyspark.pandas as ps df = ps.to_pandas(spark_df)
• spark_df.to_sql()
• import pandas as pddf = pd.DataFrame(spark_df)
• spark_df.to_pandas()

A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df.They are using the following code block to evaluate the model: regression_evaluator.setMetricName("rmse").evaluate(preds_df)Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?

• They should exponentiate the computed RMSE value
• They should take the log of the predictions before computing the RMSE
• They should evaluate the MSE of the log predictions to compute the RMSE
• They should exponentiate the predictions before computing the RMSE

A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.Which of the following compute tools is best suited for this use case?

• Single Node cluster
• Standard cluster
• SQL Warehouse
• None of these compute tools support this task

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?

• spark_df.describe()
• dbutils.data(spark_df).summarize()
• This task cannot be accomplished in a single line of code.
• spark_df.summary()
• dbutils.data.summarize (spark_df)