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NEW QUESTION: 1
What defines the portion of work included in a contract for items being purchased or acquired?
A. Procurement management plan
B. Evaluation criteria
C. Work Breakdown Structure (WBS)
D. Contract Statement of Work (SOW)
Answer: D
NEW QUESTION: 2
Following a recent data breach, a company has hired a new Chief Information Security Officer (CISO). The CISO is very concerned about the response time to the previous breach and wishes to know how the security team expects to react to a future attack. Which of the following is the BEST method to achieve this goal while minimizing disruption?
A. Recreate the previous breach.
B. Conduct an external vulnerability assessment.
C. Hire an external red team audit
D. Perform a black box assessment
E. Conduct a tabletop exercise.
Answer: E
NEW QUESTION: 3
セキュリティチームは、HTTP（TCPポート80）への公衆インターネットアクセスはないと判断しました。これは、すべての着信WebトラフィックがHTTPSに移行しているためです。チームはSysOps管理者に、準拠していないセキュリティグループに関するレポートを提供するよう依頼しました。
SysOps管理者は、ほぼリアルタイムのコンプライアンスレポートを提供するために何をすべきですか？
A. AWS Configを使用してrestricted-common-portsルールを有効にし、パラメーターにポート80を追加します。
B. AWS Trusted Advisorを有効にして、セキュリティグループの無制限アクセスチェックによって警告されることをセキュリティチームに示します。
C. Amazon Inspectorを使用してスキャン中にセキュリティグループを評価し、完成したレポートをセキュリティチームに送信します。
D. すべてのセキュリティグループをスキャンおよび評価するために1時間ごとに実行されるようにAWS Lambda関数をスケジュールし、セキュリティチームにレポートを送信します。
Answer: C
NEW QUESTION: 4
Azure Machine Learning Studioを使用して、バイナリ分類モデルを作成します。
モデルのパラメータースイープを実行して、ハイパーパラメーターを調整する必要があります。パラメータスイープは、次の要件を満たしている必要があります。
*ハイパーパラメーターの可能なすべての組み合わせを反復する
*スイープの実行に必要なコンピューティングリソースを最小限に抑える
*モデルのパラメータースイープを実行する必要があります。
どのパラメータースイープモードを使用する必要がありますか？
A. グリッド全体
B. ランダムシード
C. ランダムスイープ
D. スイープクラスタリング
E. ランダムグリッド
Answer: E
Explanation:
Explanation
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.