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NEW QUESTION: 1
A project team member decides that engaging the customer on some alternative designs is unnecessary if all functionalities are delivered The entire team decides to only engage the technical department on the best product design After user acceptance testing, the customer complains about the selected design To ensure that the customer accepts the project deliverables what should the project manager do?
A. Communicate to the customer that the product design is determined by the technical team.
B. Ensure that both the customer and technical team collaboratively select the new product design
C. Ask the customer to accept the project deliverables, as they meet all agreed-upon functional requirements
D. Update the issue log with the design issue and continue with project execution
Answer: B

NEW QUESTION: 2
金融機関は、Red Hat Enterprise Linux 7.4およびWindows Server 2016のセキュリティ強化されたAMIを、アプリケーションチームがデプロイメントで使用できるように提供しています。 DevOpsエンジニアは、各AMIの自動化された毎日のチェックを実装して、最新のCVEを監視する必要があります。エンジニアはAmazon Inspectorを使用してこれらのチェックをどのように実装する必要がありますか？
A. 各AMIにSecurityCheck：Trueのタグを付けます。 SecurityCheck：Trueのタグが付いたすべてのAMIに対して毎日1回実行されるように、スケジュールされたAmazon Inspector評価を実装します。 Amazon Inspectorは、AMIごとにAmazon EC2インスタンスを自動的に起動し、セキュリティ評価を実行する必要があります。
B. 各AMIにSecurityCheck：Trueのタグを付けます。 AWS Step Functionsを構成して、最初にタグSecurityCheck：Trueを持つすべてのAMIのAmazon Inspector評価テンプレートを作成し、次にAmazon Inspector APIアクションStartAssessmentRunを呼び出します。毎日1回ステップ関数をトリガーする、スケジュールされたAmazon CloudWatchイベントルールを実装します。
C. 各AMIにAmazon Inspectorエージェントをインストールします。強化されたAMIから各オペレーティングシステムのAmazon EC2インスタンスを起動するようにAWS Step Functionsを設定し、インスタンスにSecurityCheck：Trueのタグを付けます。 EC2インスタンスが起動すると、Step FunctionsはタグSecurityCheck：Trueのすべてのインスタンスに対してAmazon Inspector評価をトリガーします。毎日1回ステップ関数をトリガーする、スケジュールされたAmazon CloudWatchイベントルールを実装します。
D. 各インスタンスにSecurityCheck：Trueのタグを付けます。 SecurityCheck：Trueのタグが付いたすべてのインスタンスに対して毎日1回実行されるように、スケジュールされたAmazon Inspector評価を実装します。 Amazon Inspectorは、各AMIのインプレースセキュリティ評価を自動的に実行する必要があります。
Answer: C
Explanation:
https://aws.amazon.com/pt/blogs/security/how-to-set-up-continuous-golden-ami-vulnerability-assessments-with-amazon-inspector/

NEW QUESTION: 3
Cisco適応型セキュリティ仮想アプライアンスでサポートされている2つの機能はどれですか？
A. クラスタリング
B. 複数のコンテキスト
C. サイト間VPN
D. Etherchannel
E. Pakベースのライセンス
F. 高可用性
Answer: C,F
Explanation:
https://www.cisco.com/c/en/us/products/collateral/security/adaptive-security-virtual-appliance- asav/datasheet-c78-733399.html

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You train a classification model by using a logistic regression algorithm.
You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a MimicExplainer.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
Instead use Permutation Feature Importance Explainer (PFI).
Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.
Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability