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Skills Covered
To nail DP-100, you will need to scrutinize the below-mentioned areas:
- Deploy and Consume Models
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
- Manage and Optimize Models
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.
- Set up Azure ML Workspace
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
- Execute Experiments & Train Models
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
Microsoft DP-100 is a certification exam that validates one’s ability to design and implement data science solutions on Azure. DP-100J exam is designed for professionals who want to enhance their skills in data science and become certified in Azure data science solutions. DP-100J exam covers a wide range of topics, including data exploration, data preparation, modeling, and deployment.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 2: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 3: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 4: Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads - Manage compute and data assets |
| Topic 5: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) Sample Questions:
AmICompute クラスターとバッチ エンドポイントを含む Azure Machine Learning ワークスペースがあります。
MLflow モデルを含むリポジトリをローカルコンピューターにクローンします。モデルをバッチエンドポイントにデプロイできることを確認する必要があります。
解決策: ワークスペースにコンピューティング リソースを追加します。
ソリューションは目標を満たしていますか?
- A. いいえ
- B. はい
Correct Answer: B 🗳️
Azure Machine Learning ワークスペースを管理します。
Python SDK v2 を使用して、トレーニング ジョブ用のコンピューティング クラスターを作成し、構成する必要があります。
可能な限り少ないプロパティを指定して、永続的な Azure Machine Learning コンピューティング リソースを作成する必要があります。
定義する必要がある 2 つのプロパティはどれですか。それぞれの正解はソリューションの一部を示しています。
注意: 正しい選択ごとに 1 ポイントが加算されます。
- A. 型
- B. サイズ
- C. 名前
- D. 最大インスタンス数
- E. 最小インスタンス数
Correct Answer: D,E 🗳️
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
ロジスティック回帰アルゴリズムを使用して分類モデルをトレーニングします。
各機能の重要度を、全体的なグローバル相対重要度値として、および特定の予測セットのローカル重要度の尺度として計算することにより、モデルの予測を説明できる必要があります。
必要なグローバルおよびローカルの特徴重要度値を取得するために使用できる説明を作成する必要があります。
解決策：MimicExplainerを作成します。
ソリューションは目標を達成していますか？
- A. いいえ
- B. はい
Correct Answer: A 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
Azure Machine Learning ワークスペースとデータセットを作成します。データセットには、大規模な糖尿病患者グループの年齢値が含まれています。SmartNoise ライブラリの dp.mean 関数を使用して、年齢値の平均を計算します。計算結果は age.mean という変数に格納します。
95 パーセントの確率で返される、リリースされた平均値の間隔範囲の値を出力する必要があります。
コードを完了する必要があります。
どのコード値を使用する必要がありますか? 回答するには、回答エリアで適切なオプションを選択してください。注: 正しい選択ごとに 1 ポイントが加算されます。
Correct Answer:
Explanation:
To complete the code to output the value of the interval range of released mean values that will be returned 95 percent of the time using the SmartNoise library, you would need to use the privacy_usage_to_accuracy method with a value of 0.95 for the interval percentage. The dp.mean function from the SmartNoise library can return this interval when the required methods and parameters are used correctly.
print(age_mean.privacy_usage_to_accuracy(0.95))
age_mean is the variable that holds the differentially private mean value calculated using the dp.mean function.
privacy_usage_to_accuracy(0.95) method is called on age_mean to get the interval range for 95% confidence.
Therefore, the selections should be:
privacy_usage_to_accuracy
0.95
This will give you the interval range of released mean values that will be returned 95 percent of the time.
注: この質問は、同じシナリオを示す一連の質問の一部です。このシリーズの各質問には、指定された目標を達成できる可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策が含まれる場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答すると、その質問に戻ることはできません。そのため、これらの質問はレビュー画面には表示されません。
Azure Machine Learning ワークスペースがあります。Azure Machine Learning Studio の [ノートブック] ページからターミナル セッションに接続します。
同じターミナル セッションからアクセスできる新しい Jupyter カーネルを追加する予定です。
新しいカーネルを追加する前に、完了する必要があるタスクを実行する必要があります。
解決策: Python 3.8 - AzureML カーネルを削除します。
解決策は目標を達成できますか?
- A. いいえ
- B. はい
Correct Answer: A 🗳️
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