ちなみに、Japancert DP-100の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1N4d783qqb1JoQKJs-l0FqmT3Vq16U9Uf
MicrosoftのDP-100試験は国際的に認可られます。これがあったら、よい高い職位の通行証を持っているようです。Japancertの提供するMicrosoftのDP-100試験の資料とソフトは経験が豊富なITエリートに開発されて、何回も更新されています。何十ユーロだけでこのような頼もしいMicrosoftのDP-100試験の資料を得ることができます。試験に合格してからあなたがよりよい仕事と給料がもらえるかもしれません。
| Section | Objectives |
|---|---|
| Topic 1: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 2: Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace - Manage compute and data assets |
| Topic 3: Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Topic 4: Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Topic 5: Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
JapancertのMicrosoftのDP-100試験トレーニング資料は君の成功に導く鍵で、君のIT業種での発展にも助けられます。長年の努力を通じて、JapancertのMicrosoftのDP-100認定試験の合格率が１００パーセントになっていました。もしうちの学習教材を購入した後、認定試験に不合格になる場合は、全額返金することを保証いたします。
質問 # 282
You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi- image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.
You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:
An authenticated connection must not be required for testing.
The deployed model must perform with low latency during inferencing.
The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.
You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.
Which compute resources should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: ds-workstation notebook VM
An authenticated connection must not be required for testing.
On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials.
Box 2: gpu-compute cluster
Image classification is well suited for GPU compute clusters
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning
質問 # 283
You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?
正解：A
解説：
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model
質問 # 284
You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.
You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.
What should you do?
正解：B
解説：
Explanation
If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments
質問 # 285
You need to configure the Permutation Feature Importance module for the model training requirements.
What should you do? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: 500
For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.
A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.
Here we must replicate the findings.
Box 2: Mean Absolute Error
Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature- importance
質問 # 286 
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 287
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DP-100受験内容: https://www.japancert.com/DP-100.html
P.S. JapancertがGoogle Driveで共有している無料かつ新しいDP-100ダンプ：https://drive.google.com/open?id=1N4d783qqb1JoQKJs-l0FqmT3Vq16U9Uf