Microsoft DP-100: Career Prospects

After successfully passing the Microsoft DP-100 exam, you will obtain the Microsoft Certified: Azure Data Scientist Associate certification. Getting certified will allow you to qualify for several positions, including the following titles:

Software Developer
Data Engineer
Administrative Analyst
Data Analyst

Obtaining this certification is also beneficial from a financial point of view. In fact, the average salary that a certified professional can earn is $96,642 per year.

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Skills measured

Manage Azure resources for machine learning (25–30%)
Deploy and operationalize machine learning solutions (35–40%)
Run experiments and train models (20–25%)
Implement responsible machine learning (5–10%)

3. Models Management and Optimization (20-25%):

Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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Step 1: Reviewing the Exam Topics

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Microsoft DP-100日本語 Exam Syllabus Topics:

Section	Weight	Objectives
Optimize language models for AI applications	25-30%	- Implement generative AI solutions 1. Build prompt flows 2. Use Azure AI Foundry 3. Apply prompt engineering - Optimize with Retrieval Augmented Generation 1. Create vector stores and indexes 2. Configure Azure AI Search 3. Prepare and process data - Evaluate and improve models 1. Optimize for accuracy and safety 2. Test and evaluate responses 3. Apply responsible generative AI
Explore data and run experiments	20-25%	- Explore and visualize data 1. Detect anomalies and outliers 2. Identify features and relationships 3. Profile and validate data - Implement pipelines 1. Pass data between steps 2. Build reusable components 3. Schedule and monitor pipelines 4. Create and publish pipelines - Run experiments 1. Use automated machine learning 2. Configure experiment runs 3. Track runs with MLflow 4. Define parameters and configurations
Train and deploy models	25-30%	- Manage models 1. Interpret models and explain predictions 2. Register and version models 3. Package and validate models - Deploy models 1. Deploy to online endpoints 2. Secure endpoints and manage access 3. Deploy to batch endpoints 4. Configure compute and scaling - Train models 1. Configure jobs and environments 2. Run training scripts 3. Use HyperDrive for hyperparameter tuning 4. Apply responsible AI principles - Monitor and maintain models 1. Implement MLOps practices 2. Update and retrain models 3. Monitor performance and data drift
Design and prepare a machine learning solution	20-25%	- Manage compute resources 1. Create and configure compute targets 2. Attach and monitor compute 3. Select environments - Manage data assets 1. Register and manage datastores 2. Create and maintain data assets 3. Select storage services - Design a machine learning solution 1. Plan model deployment requirements 2. Determine dataset structure and format 3. Define compute specifications for workloads 4. Select development approach - Manage Azure Machine Learning workspace 1. Use developer tools and CLI 2. Create and configure workspace 3. Work with registries 4. Set up Git integration

Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100日本語版) Sample Questions:

Question #1

Azure Machine Learning プロジェクトファイルを含む既存の GitHub リポジトリがあります。
リポジトリを Azure Machine Learning 共有ワークスペースファイルシステムに複製する必要があります。
どの 4 つのアクションを順番に実行する必要がありますか? 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。
注意：正解の選択肢は複数あります。正解の選択肢の中からどれを選択しても、得点は加算されます。

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Question #2

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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。
機械学習モデルをトレーニングして登録します。
モデルをリアルタイムWebサービスとしてデプロイすることを計画しています。モデルを使用するには、アプリケーションでキーベースの認証を使用する必要があります。
Webサービスをデプロイする必要があります。
解決：
AksWebserviceインスタンスを作成します。
auth_enabledプロパティの値をTrueに設定します。
モデルをサービスにデプロイします。
ソリューションは目標を達成していますか？

A. はい
B. いいえ

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #3

Azure Machine Learningワークスペースを作成します
ワークスペースでカスタムモデルのトレーニングを実行するためのPython SDK v2ノートブックを開発しています。ノートブックのコードは必要なパッケージをすべてインポートします。
トレーニングスクリプト、環境、およびコンピューティング情報を含めるように Python SDK v2 コードを完成させる必要があります。
10 個のコードをどのように完成させるべきでしょうか? 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: 正解ごとに1ポイント獲得