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NEW QUESTION: 1
In which portion of the frame is the 802.1q header found?
A. within the Ethernet source MAC address
B. within the Ethernet FCS
C. within the Ethernet payload
D. within the Ethernet header
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Frame format

Insertion of 802.1Q tag in an Ethernet frame
802.1Q does not encapsulate the original frame. Instead, for Ethernet frames, it adds a 32-bit field between the source MAC address and the EtherType/length fields of the original frame Reference: http:// en.wikipedia.org/wiki/IEEE_802.1Q

NEW QUESTION: 2
A user contacts the help desk after being unable to log in to a corporate website. The user can log into the site from another computer in the next office, but not from the PC. The user's PC was able to connect earlier in the day. The help desk has user restart the NTP service. Afterwards the user is able to log into the website. The MOST likely reason for the initial failure was that the website was configured to use which of the following authentication mechanisms?
A. Kerberos
B. RADIUS
C. NTLMv2
D. Secure LDAP
Answer: A

NEW QUESTION: 3
あなたは銀行で働いているデータサイエンティストであり、Azure MLを使用して、顧客がローンを返済する可能性が高いかどうかを予測する機械学習モデルをトレーニングして登録しました。
モデルがどのように選択を行っているかを理解し、モデルが申請者の居住地に基づいてローンを拒否するなどの政府の規制に違反しないことを確認する必要があります。
顧客データの各機能が予測にどの程度影響を与えているかを判断する必要があります。
あなたは何をするべきか？
A. モデル登録にタグを追加して、トレーニングデータセットの特徴の名前を示します。
B. 既知のラベル値を持ついくつかのテストデータに対してモデルをスコアリングし、その結果を使用して混同行列を計算します。
C. 解釈性パッケージを使用して、モデルの説明を生成します。
D. Hyperdriveライブラリを使用して、複数のハイパーパラメーター値でモデルをテストします。
E. モデルとそのトレーニングデータセットのデータドリフトモニタリングを有効にします。
Answer: C
Explanation:
When you compute model explanations and visualize them, you're not limited to an existing model explanation for an automated ML model. You can also get an explanation for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.
Incorrect Answers:
A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons where model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.
B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.
C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl

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