최신 Databricks-Machine-Learning-Associate 무료덤프 - Databricks Certified Machine Learning Associate

문제1

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

A. MLflow Experiment Tracking

B. Spark ML

C. Autoscaling clusters

D. Autoscaling clusters

E. Delta Lake

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제2

A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.
Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

A. Spark SQL

B. pandas API on Spark

C. Feature Store

D. PySpark DataFrame API

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제3

Which of the following machine learning algorithms typically uses bagging?

A. Decision tree

B. Random forest

C. IGradient boosted trees

D. K-means

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제4

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?

A. Remove the mean operation that is wrapping the cross_val_score operation

B. Replace the fmin operation with the fmax operation

C. Add a random_state argument to the RandomForestRegressor operation

D. Replace the r2 return value with -r2

E. Add test set validation process

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제5

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?

A. Replace the GridSearchCV operation with RandomizedSearchCV

B. Replace the GridSearchCV operation with ParameterGrid

C. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')

D. Replace the GridSearchCV operation with cross_validate

E. Replace the random_state=0 argument with random_state=1

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제6

A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

A. Bias is avoidable when using a train-validation split

B. Fewer models need to be trained when using a train-validation split

C. Reproducibility is achievable when using a train-validation split

D. A holdout set is not necessary when using a train-validation split

E. Fewer hyperparameter values need to be tested when using a train-validation split

정답: B

문제7

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?

A. This task cannot be accomplished in a single line of code.

B. dbutils.data.summarize (spark_df)

C. spark_df.describe()

D. dbutils.data(spark_df).summarize()

E. spark_df.summary()

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제8

A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of including pipeline as the estimator in the cross-validation process rather than rfr as the estimator?

A. The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode

B. The process will leak data prep information from the validation sets to the training sets for each model

C. The process will leak data from the training set to the test set during the evaluation phase

D. The process will be unable to parallelize tuning due to the distributed nature of pipeline

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

최신 Databricks-Machine-Learning-Associate 무료덤프 - Databricks Certified Machine Learning Associate

우리와 연락하기

유용한 링크

최신 업데이트