Valid Microsoft DP-100 Questions - Prepare Effectively For Exam

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Microsoft DP-100 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Optimize language models for AI applications25-30%- Implement generative AI solutions
  • 1. Build prompt flows
  • 2. Use Azure AI Foundry
  • 3. Apply prompt engineering
- Evaluate and improve models
  • 1. Test and evaluate responses
  • 2. Apply responsible generative AI
  • 3. Optimize for accuracy and safety
- Optimize with Retrieval Augmented Generation
  • 1. Configure Azure AI Search
  • 2. Prepare and process data
  • 3. Create vector stores and indexes
Topic 2: Design and prepare a machine learning solution20-25%- Manage data assets
  • 1. Select storage services
  • 2. Create and maintain data assets
  • 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. Work with registries
  • 2. Create and configure workspace
  • 3. Set up Git integration
  • 4. Use developer tools and CLI
- Design a machine learning solution
  • 1. Define compute specifications for workloads
  • 2. Determine dataset structure and format
  • 3. Select development approach
  • 4. Plan model deployment requirements
Topic 3: Train and deploy models25-30%- Train models
  • 1. Run training scripts
  • 2. Apply responsible AI principles
  • 3. Configure jobs and environments
  • 4. Use HyperDrive for hyperparameter tuning
- Monitor and maintain models
  • 1. Update and retrain models
  • 2. Implement MLOps practices
  • 3. Monitor performance and data drift
- Manage models
  • 1. Register and version models
  • 2. Package and validate models
  • 3. Interpret models and explain predictions
- Deploy models
  • 1. Deploy to batch endpoints
  • 2. Configure compute and scaling
  • 3. Secure endpoints and manage access
  • 4. Deploy to online endpoints
Topic 4: Explore data and run experiments20-25%- Implement pipelines
  • 1. Build reusable components
  • 2. Schedule and monitor pipelines
  • 3. Create and publish pipelines
  • 4. Pass data between steps
- Run experiments
  • 1. Define parameters and configurations
  • 2. Configure experiment runs
  • 3. Track runs with MLflow
  • 4. Use automated machine learning
- Explore and visualize data
  • 1. Identify features and relationships
  • 2. Profile and validate data
  • 3. Detect anomalies and outliers

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q79-Q84):

NEW QUESTION # 79
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contain missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points.
Does the solution meet the goal?

Answer: B

Explanation:
Explanation
Instead use the Multiple Imputation by Chained Equations (MICE) method.
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or
"Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Last observation carried forward (LOCF) is a method of imputing missing data in longitudinal studies. If a person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused by the attrition of participants in a study.
References:
https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/


NEW QUESTION # 80
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio


NEW QUESTION # 81
You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.
You start by writing the following code segment:

For each of the following statements, select Yes If the statement is true. Otherwise, select No.

Answer:

Explanation:

Explanation:


NEW QUESTION # 82

You need to obtain the output from the pipeline execution. Where will you find the output?

Answer: B

Explanation:
output_action (str): How the output is to be organized. Currently supported values are 'append_row' and
'summary_only'.
'append_row' - All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.
parallelrunconfig


NEW QUESTION # 83
You load data from a notebook in an Azure Machine Learning workspace into a panda's cat frame. The data contains 10.000 records. Each record consists of 10 columns.
You must identify the number of missing values in each of the columns.
You need to complete the Python code that will return the number of missing values in each of the columns.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE; Each correct selection it worth one point.

Answer:

Explanation:

Explanation


NEW QUESTION # 84
......

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