ML-ASSOC practice questions 11–20

1. Question 11 · 45 in bank

   What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

   • ALeave-one-out encoding
   • BTarget encoding
   • COne-hot encoding
   • DCategorical embeddings
   • EString indexing

   Show answer

   Answer: C

   Next question

2. Question 12 · 45 in bank

   A data scientist wants to parallelize the training of trees in a gradient boosted tree to speed up the training process. A colleague suggests that parallelizing a boosted tree algorithm can be difficult. Which of the following describes why?

   • AGradient boosting is not a linear algebra-based algorithm which is required for parallelization.
   • BGradient boosting requires access to all data at once which cannot happen during parallelization.
   • CGradient boosting calculates gradients in evaluation metrics using all cores which prevents parallelization.
   • DGradient boosting is an iterative algorithm that requires information from the previous iteration to perform the next step.
   • EGradient boosting uses decision trees in each iteration which cannot be parallelized.

   Show answer

   Answer: D

   Next question

3. Question 13 · 45 in bank

   A data scientist wants to efficiently tune the hyperparameters of a scikit-learn model. They elect to use the Hyperopt library's fmin operation to facilitate this process. Unfortunately, the final model is not very accurate. The data scientist suspects that there is an issue with the objective_function being passed as an argument to fmin. They use the following code block to create the objective_function:

   

   Which of the following changes does the data scientist need to make to their objective_function in order to produce a more accurate model?

   • AAdd test set validation process
   • BAdd a random_state argument to the RandomForestRegressor operation
   • CRemove the mean operation that is wrapping the cross_val_score operation
   • DReplace the r2 return value with -r2
   • EReplace the fmin operation with the fmax operation

   Show answer

   Answer: D

   Next question

4. Question 14 · 45 in bank

   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?

   • AReplace the GridSearchCV operation with RandomizedSearchCV
   • BReplace the GridSearchCV operation with cross_validate
   • CReplace the GridSearchCV operation with ParameterGrid
   • DReplace the random_state=0 argument with random_state=1
   • EReplace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')

   Show answer

   Answer: A

   Next question

5. Question 15 · 45 in bank

   Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?

   • AMLflow Experiment Tracking
   • BSpark ML
   • CAutoscaling clusters
   • DHyperopt
   • EDelta Lake

   Show answer

   Answer: D

   Next question

6. Question 16 · 45 in bank

   Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

   • Apandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
   • Bpandas API on Spark DataFrames are more performant than Spark DataFrames
   • Cpandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
   • Dpandas API on Spark DataFrames are less mutable versions of Spark DataFrames
   • Epandas API on Spark DataFrames are unrelated to Spark DataFrames

   Show answer

   Answer: C

   Next question

7. Question 17 · 45 in bank

   A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data. Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

   • AThey can refactor their notebook to process the data in parallel.
   • BThey can refactor their notebook to use the PySpark DataFrame API.
   • CThey can refactor their notebook to use the Scala Dataset API.
   • DThey can refactor their notebook to use Spark SQL.
   • EThey can refactor their notebook to utilize the pandas API on Spark.

   Show answer

   Answer: E

   Next question

8. Question 18 · 45 in bank

   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?

   • Apredict(*spark_df.columns)
   • BmapInPandas(predict)
   • Cpredict(Iterator(spark_df))
   • DmapInPandas(predict(spark_df.columns))
   • Epredict(spark_df.columns)

   Show answer

   Answer: A

   Next question

9. Question 19 · 45 in bank

   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?

   • ATrainValidationSplit
   • BDataFrame.where
   • CCrossValidator
   • DTrainValidationSplitModel
   • EDataFrame.randomSplit

   Show answer

   Answer: E

   Next question

10. Question 20 · 45 in bank

    A data scientist is using Spark ML to engineer features for an exploratory machine learning project. They decide they want to standardize their features using the following code block:

    

    Upon code review, a colleague expressed concern with the features being standardized prior to splitting the data into a training set and a test set. Which of the following changes can the data scientist make to address the concern?

    • AUtilize the MinMaxScaler object to standardize the training data according to global minimum and maximum values
    • BUtilize the MinMaxScaler object to standardize the test data according to global minimum and maximum values
    • CUtilize a cross-validation process rather than a train-test split process to remove the need for standardizing data
    • DUtilize the Pipeline API to standardize the training data according to the test data's summary statistics
    • EUtilize the Pipeline API to standardize the test data according to the training data's summary statistics

    Show answer

    Answer: E

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

ML-ASSOC practice, question 11. 45 questions available.