Databricks Databricks-Machine-Learning-Associate 問題集

質問 1：
Which of the following approaches can be used to view the notebook that was run to create an MLflow run?
A. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page
B. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
C. Open the MLmodel artifact in the MLflow run paqe
D. Click the "Source" link in the row corresponding to the run in the MLflow experiment page
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?
A. The cross-validation process will no longer be reproducible
B. The model will take longer to train for each unique combination of hvperparameter values
C. The feature engineering stages will be computed using validation data
D. The model will be refit one more per cross-validation fold
E. The cross-validation process will no longer be
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?
A. predict(*spark_df.columns)
B. predict(spark_df.columns)
C. predict(Iterator(spark_df))
D. mapInPandas(predict(spark_df.columns))
E. mapInPandas(predict)
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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?
A. Manually partition the input data
B. Write out the split data sets to persistent storage
C. Set a speed in the data splitting operation
D. Manually configure the cluster
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?
A. The vectorized pandas UDFs process data in memory rather than spilling to disk
B. The vectorized pandas UDFs process data in batches rather than one row at a time
C. The vectorized pandas UDFs allow for pandas API use inside of the function
D. The vectorized pandas UDFs allow for the use of type hints
E. The vectorized pandas UDFs work on distributed DataFrames
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A data scientist is utilizing MLflow Autologging to automatically track their machine learning experiments. After completing a series of runs for the experiment experiment_id, the data scientist wants to identify the run_id of the run with the best root-mean-square error (RMSE).
Which of the following lines of code can be used to identify the run_id of the run with the best RMSE in experiment_id?
A.

B.

C.

D.

正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 7：
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?
A. Standard cluster
B. Single Node cluster
C. SQL Warehouse
D. None of these compute tools support this task
正解：A
解説: (Topexam メンバーにのみ表示されます)