Databricks DATABRICKS-MACHINE-LEARNING-ASSOCIATE Exam Questions & Answers

Correct Answer: C

The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use theRegressionEvaluatorclass withmetricNameset to "rmse". Given the schema ofpreds_dfwith

columnspredictionandactual, the correct evaluator setup will specifypredictionCol="prediction"andlabelCol="actual". Thus, the appropriate code block (Option C in your list) that usesRegressionEvaluatorto compute the RMSE is the correct

choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.

References:

Spark ML documentation (Using RegressionEvaluator to Compute RMSE).

Correct Answer: C

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.

References:

MLflow Documentation (Viewing Run Sources and Notebooks).

Correct Answer: C

To filter rows in a Spark DataFrame based on a condition, thefiltermethod is used. In this case, the condition is that the value in the "discount" column should be less than or equal to 0. The correct syntax uses thefiltermethod along with

thecolfunction from pyspark.sql.functions.

Correct code:

frompyspark.sql.functionsimportcol filtered_df = spark_df.filter(col("discount") <=0) Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of thecolfunction.

References:

PySpark SQL Documentation