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NEW QUESTION: 1
Inputs to the Define Activities process include:
A. Project scope statement, approved change requests, and WBS dictionary.
B. Scope baseline, enterprise environmental factors, and organizational process assets.
C. Scope baseline, enterprise environmental factors, and activity duration estimates.
D. Project scope statement, resource calendars, and work performance information.
Answer: B
NEW QUESTION: 2
You've written a MapReduce job that will process 500 million input records and generated 500 million
key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant
amount of intermediate data that it needs to transfer between mappers and reduces which is a potential
bottleneck. A custom implementation of which interface is most likely to reduce the amount of
intermediate data transferred across the network?
A. WritableComparable
B. OutputFormat
C. Writable
D. Partitioner
E. Combiner
F. InputFormat
Answer: E
NEW QUESTION: 3
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer
You need to explain the predictions made by the model for all classes by determining the importance of all features.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).
Box 2: Yes
To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.
Box 3: No
TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml
NEW QUESTION: 4
最も正確な大まかな順序(ROM)とは何ですか?
A. 開始フェーズでは、推定値は+/- 50%の範囲にあります。
B. 計画段階では、推定値の範囲は+/- 50%です。
C. 監視および制御フェーズでは、推定値は+/- 15%の範囲にあります。
D. 終了フェーズでは、推定値は+/- 15%の範囲にあります。
Answer: A