Databricks Databricks-Machine-Learning-Associate 問題集

質問 1：
Which of the following machine learning algorithms typically uses bagging?
A. Decision tree
B. K-means
C. IGradient boosted trees
D. Random forest
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
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 3：
A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.
The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?
A. They need to call the transform method on train df
B. They need to convert the features column to be a vector
C. They do not need to make any changes
D. They need to split the features column out into one column for each feature
E. They need to utilize a Pipeline to fit the model
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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 5：
A machine learning engineer has created a Feature Table new_table using Feature Store Client fs. When creating the table, they specified a metadata description with key information about the Feature Table. They now want to retrieve that metadata programmatically.
Which of the following lines of code will return the metadata description?
A. fs.get_table("new_table")
B. There is no way to return the metadata description programmatically.
C. fs.get_table("new_table").load_df()
D. fs.get_table("new_table").description
E. fs.create_training_set("new_table")
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A data scientist is working with a feature set with the following schema:

The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each feature.
Which of the following lists all of the columns in the feature set that need to be imputed using the most common value of the column?
A. loyalty_tier
B. spend
C. units
D. customer_id
E. customer_id, loyalty_tier
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 7：
Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?
A. Grid Search
B. Halving Random Search
C. Random Search
D. Tree of Parzen Estimators
正解：D
解説: (Topexam メンバーにのみ表示されます)