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Microsoft DP-100 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q272-Q277):

NEW QUESTION # 272
You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.
You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.
What should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Vocabulary mode: Create
For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.
N-Grams size: 3
For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.
Weighting function: Leave blank
The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features-from-text

NEW QUESTION # 273
You use the Azure Machine Learning Python SDK to define a pipeline to train a model.
The data used to train the model is read from a folder in a datastore.
You need to ensure the pipeline runs automatically whenever the data in the folder changes.
What should you do?

A. Create a PipelineParameter with a default value that references the location where the training data is stored
B. Set the regenerate_outputs property of the pipeline to True
C. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datastore property
D. Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline

Answer: C

Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline

NEW QUESTION # 274
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute! by using a terminal window from wofkspace1. You create a file named "requirements.txt" containing Python dependencies to include Jupyler.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - conda create -n "python_env"
2 - conda activate "python_env"
3 - conda install ~r "requirements.txt"
4 - ipython kernel install ~~user ~~name= "python_env"

NEW QUESTION # 275
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent).
The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION # 276
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?

A. Yes
B. No

Answer: B

NEW QUESTION # 277
......

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