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Candidates for the Azure Data Scientist Associate certification should have subject matter expertise applying data science and machine learning to implement and run machine learning workloads on Azure.
Responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. You run data experiments and train predictive models. In addition, you manage, optimize, and deploy machine learning models into production.
A candidate for this certification should have knowledge and experience in data science and using Azure Machine Learning and Azure Databricks.
Part of the requirements for: Microsoft Certified: Azure Data Scientist Associate
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Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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The demand for the certified professionals is on the increase. As an Azure Data Scientist, you can explore many employment opportunities. So, if you want to boost your career potential, pursuing the Microsoft Certified: Azure Data Scientist Associate certification is the right step. To get this certificate, you have to pass the qualifying exam, which is Microsoft DP-100. With this associate-level certification, you can get the positions, such as a Data Scientist, a Senior Data Scientist, a Data Science Manager, and a Data Science Director. The average salary for these job roles is $135,000 per annum. With advanced experience, the specialists can earn up to $170,000 per year.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explore data and run experiments | 20-25% | - Implement pipelines
|
| Topic 2: Train and deploy models | 25-30% | - Manage models
|
| Topic 3: Design and prepare a machine learning solution | 20-25% | - Manage data assets
|
| Topic 4: Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
|
Azure Machine Learning Studioの2クラスニューラルネットワークモジュールを使用して、バイナリ分類モデルを構築します。 Tune Model Hyperparametersモジュールを使用して、モデルの精度を調整します。
Tune Model Hyperparametersモジュールを使用して、調整する必要があるハイパーパラメーターを選択する必要があります。
どの2つのハイパーパラメーターを使用する必要がありますか?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: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。
Correct Answer: B,E 🗳️
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バイナリ分類を実行するためにリカレント ニューラル ネットワークを構築しています。
各トレーニングエポックのトレーニング損失、検証損失、トレーニング精度、検証精度が提供されています。分類モデルが過剰適合していないかどうかを確認する必要があります。
次のどれが正しいでしょうか?
Correct Answer: C 🗳️
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複数の生徒に実践的なワークショップを実施する予定です。ワークショップでは、Pythonを使用したデータ視覚化の作成に焦点を当てます。各生徒は、インターネットにアクセスできるデバイスを使用します。
学生用デバイスはPython開発用に構成されていません。学生には、デバイスにソフトウェアをインストールするための管理者アクセス権がありません。学生はAzureサブスクリプションを利用できません。
学生がPythonベースのデータ視覚化コードを実行できることを確認する必要があります。
どのAzureツールを使用する必要がありますか?
Correct Answer: C 🗳️
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Azure Machine Learning Studio で多重線形回帰モデルを作成しています。
いくつかの独立変数は高度に相関しています。
すべてのデータに対して効果的な特徴エンジニアリングを実行するには、適切な方法を選択する必要があります。
順番に実行する必要がある 3 つのアクションはどれですか。回答するには、適切なアクションをアクション リストから回答領域に移動し、正しい順序に並べます。
Correct Answer:

Explanation:
Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature- selection
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform
Azure Machine Learning ワークスペースがあります。
Azure Machine Learning Python SDK v2 を使用してワークスペースにコンポーネントを登録する予定です。コンポーネント定義は、ローカル ファイル ./components/train/train.yml に保存されます。
ml_client オブジェクトを使用してワークスペースに接続し、必要なすべてのライブラリをインポートするコードを記述します。残りのコードを完成させる必要があります。
コードをどのように完成させるべきですか? 回答するには、回答領域で適切なオプションを選択してください。
注意: 正しい選択ごとに 1 ポイントが加算されます。
Correct Answer:

Explanation:
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