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NEW QUESTION: 1
You are designing an Extract, Transform and Load (ETL) solution that loads data into dimension tables.
The ETL process involves many transformation steps.
You need to ensure that the design can provide:
Auditing information for compliance and business user acceptance
Tracking and unique identification of records for troubleshooting and error correction What should you do?
A. Create a version control repository for the ETL solution.
B. Develop a Data Quality Services (DQS) solution.
C. Develop a Master Data Services (MDS) solution.
D. Develop a custom data lineage solution.
Answer: D

NEW QUESTION: 2
What function can be used with SQL to encrypt user suppliedpasswords so that they can be compared with the encrypted passwords stored on the SQL server?
A. userencrypt()
B. DESpassword()
C. pwdencrypt()
D. passwordin()
Answer: C

NEW QUESTION: 3
展示を参照してください。 現在、メトリックが10の10.0.0.0/8ネットワークへのルートが少なくとも1つあります。この構成を適用した結果を説明するステートメントはどれですか。
A. ルーターは、メトリックが2を超えるパスで等コストのロードバランシングのみを実行します。
B. メトリックが20未満の最初の4つのパスがルーティングテーブルに追加されます。
C. メトリックが10のパスが2つ、メトリックが20のパスが2つある場合、合計2つのパスが使用されます。
D. メトリックが10のパスが1つ、メトリックが20のパスが1つある場合、合計2つのパスが使用されます。
Answer: D

NEW QUESTION: 4
Azure Machine Learning Studioで実験を作成します。 10,000行を含むトレーニングデータセットを追加します。最初の9,000行はクラス0（90パーセント）を表します。
残りの1,000行はクラス1（10パーセント）を表します。
トレーニングセットは、2つのクラス間の不均衡です。 5つのデータ行を使用して、クラス1のトレーニング例の数を4,000に増やす必要があります。 Synthetic Minority Oversampling Technique（SMOTE）モジュールを実験に追加します。
モジュールを構成する必要があります。
どの値を使用する必要がありますか？回答するには、回答領域のダイアログボックスで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote