Conclusion
Data science is a lucrative career option and you can explore it without any hassle if you’ve managed to taste success in the Microsoft DP-100 exam, which is known to create a skilled workforce of Azure data scientists. However, a great outcome in such a test will only come if the aspirant is referring to the updated and recent study resources like the official courses provided by the exam vendor itself.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Ways to Getting DP-100 Exam-Ready
The kind of extensive subject matter for the Microsoft DP-100 can be only obtained once the aspirant has access to quality study resources that are in general endorsed by the vendor itself. Overall, Microsoft offers both free and paid training courses to gain demanded expertise for DP-100. When it comes to the gratis learning path, it contains the following self-paced classes:
- Create Machine Learning Model - This is an intermediate-level free training course with 5 modules that details every single fact you should know about ML and its functions, features, and tools. To enroll for this option, it’s advisable that you have some related experience with the Python programming language.
- Create No-Code Predictive Models with Azure Machine Learning - In all, it’s beginner training that is composed of 4 concise modules that accentuate such technologies as data science, ML, Azure, and AI engineering. To be entitled to access this learning path and entirely apprehend all the details covered here, you are expected to have some skills in navigating the Azure portal.
- Build AI Solutions with Machine Learning - This is the mid-tier class offered for those who want to get themselves familiarized with Azure ML Python SDK that is used for creating organizational-ready AI solutions. Consisting of 14 full-length parts, the training requires you to possess some experience dealing with Python, PyTorch, ML, Scikit-Learn, and Tensorflow.
As far as you can see, all these are the best options to consider when free learning resources for the Microsoft DP-100 exam are concerned. Still, there is one paid instructor-led training course as well. It covers the concepts related to Designing & Implementing a Data Science Solution on Azure and is known as Course DP-100T01-A, which is a 3-day long class explaining every bit of the exam domains. In particular, the course imparts in applicants the intermediate knowledge of ML solutions, Python to monitor data preparation & ingestion, and ML frameworks. During the whole training, you’ll get exposed to as many as 10 comprehensive modules that spin around the test objectives. What makes these lessons significant is that they contain labs in the curriculum unlike free self-paced learning, which allows you to develop practical skills that are generally necessary for job-oriented tasks. So, visit the vendor’s website, check whether you comply with the course prerequisites and if you do, enroll in it, and be entirely geared up for the Microsoft DP-100 exam.
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What is the duration of the DP-100 Exam
- Number of Questions: 45-60
- Passing Score: 700/1000
- Length of Examination: 150 minutes
- Format: Multiple choices, multiple answers
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Microsoft DP-100 Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Topic 2: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Topic 3: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 4: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 5: 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 |
Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100 Korean Version) Sample Questions:
다양한 도시의 주택 소유 관련 인구 통계 데이터를 조사할 계획입니다. 데이터는 다음과 같은 형식의 CSV 파일에 있습니다.
나이, 도시, 소득, 주택 소유자
21,시카고,50000,0
35,시애틀,120000,1
23,시애틀,65000,0
45,시애틀,130000,1
18,시카고,48000,0
데이터를 탐색하고 결과를 기록하려면 Azure Machine Learning 작업 영역에서 실험을 실행해야 합니다. 실험에는 다음 정보가 기록되어야 합니다.
데이터 세트의 관측치 수
주택 소유자별 소득의 상자 그림
도시 이름과 각 도시의 평균 소득이 포함된 딕셔너리입니다. 필요한 정보를 기록하려면 실험 실행 객체의 적절한 로깅 메서드를 사용해야 합니다.
코드를 어떻게 완성해야 할까요? 정답을 맞추려면, 적절한 코드 조각을 올바른 위치로 드래그하세요.
각 코드 세그먼트는 한 번, 여러 번 또는 전혀 사용되지 않을 수 있습니다. 콘텐츠를 보려면 창 사이의 분할 막대를 드래그하거나 스크롤해야 할 수 있습니다.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
Box 1: log
The number of observations in the dataset.
run.log(name, value, description= ' ' )
Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log( " accuracy " , 0.95)
Box 2: log_image
A box plot of income by home_owner.
log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.
Example: run.log_image( " ROC " , plot=plt)
Box 3: log_table
A dictionary containing the city names and the average income for each city.
log_table: Log a dictionary object to the run with the given name.
Azure Machine Learning 작업 영역에 여러 머신 러닝 모델이 등록되어 있습니다.
선택한 모델의 공정성을 평가하려면 Fairlearn 대시보드를 사용해야 합니다.
어떤 세 가지 행동을 순서대로 수행해야 할까요? 정답을 선택하려면, 행동 목록에서 적절한 행동을 골라 답란에 옮겨 올바른 순서대로 배열하세요.
Correct Answer:
Explanation:
Step 1: Select a model feature to be evaluated.
Step 2: Select a binary classification or regression model.
Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.
Example:
model_dict = {}
lr_reg_id = register_model( " fairness_logistic_regression " , lr_predictor) model_dict[lr_reg_id] = lr_predictor svm_reg_id = register_model( " fairness_svm " , svm_predictor) model_dict[svm_reg_id] = svm_predictor Step 3: Select a metric to be measured Precompute fairness metrics.
Create a dashboard dictionary using Fairlearn ' s metrics package.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml
머신러닝 모델이 민감한 특징에 대해 불공정한 예측을 내놓을 수 있습니다.
모델의 불공정성을 완화하기 위해 후처리 기법을 사용하여 모델에 제약을 적용해야 합니다.
후처리 기법과 모델 유형을 선택해야 합니다.
무엇을 사용해야 할까요? 답변하려면 답변란에서 적절한 옵션을 선택하세요.
참고: 정답 하나당 1점입니다.
Correct Answer:
Explanation:
Azure Machine Learning 작업 영역을 관리합니다. scriptpy라는 이름의 Pylhon 스크립트는 training_data라는 인수를 읽습니다. trainlng.data 인수는 datasetl이라는 파일에 있는 학습 데이터의 경로를 지정합니다.
CSV.
당신은 scriptpy 파이썬 스크립트를 머신러닝 모델을 학습시키는 명령 작업으로 실행할 계획입니다.
스크립트를 학습 작업으로 제출할 때 datasct의 경로를 매개변수 값으로 전달하는 명령을 제공해야 합니다.
해결 방법: python train.py --training_data training_data
이 해결책은 목표를 달성합니까?
- A. 아니요
- B. 예
Correct Answer: A 🗳️
Azure Machine Learning 작업 영역을 생성합니다.
워크스페이스의 Jupyter 노트북에서 실험을 구현하려면 Python SDK v2를 사용해야 합니다. 실험은 문자열 메트릭을 로깅해야 합니다. 문자열 메트릭을 로깅하는 메서드를 구현해야 하는데, 어떤 메서드를 사용해야 할까요?
- A. mlflow.log 텍스트()
- B. mlflowlog_metrk()
- C. mlflow.log.dict()
- D. mlflow.log_artifact()
Correct Answer: A 🗳️
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