Practice Exams | MS Azure DP-100 Design & Implement DS Sol

Be prepared for the MS Azure Exam DP-100: Designing and Implementing a Data Science Solution on Azure

Description

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item “B”  last time you went through the test.

NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.

Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.

Audience profile

As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI.

Your responsibilities for this role include:

• Designing and creating a suitable working environment for data science workloads.

• Exploring data.

• Training machine learning models.

• Implementing pipelines.

• Running jobs to prepare for production.

• Managing, deploying, and monitoring scalable machine learning solutions.

• Using language models for building AI applications.

As a candidate for this exam, you should have knowledge and experience in data science by using:

• Azure Machine Learning

• MLflow

• Azure AI services, including Azure AI Search

• Azure AI Foundry

Skills at a glance

• Design and prepare a machine learning solution (20–25%)

• Explore data, and run experiments (20–25%)

• Train and deploy models (25–30%)

• Optimize language models for AI applications (25–30%)

Design and prepare a machine learning solution (20–25%)

Design a machine learning solution

• Identify the structure and format for datasets

• Determine the compute specifications for machine learning workload

• Select the development approach to train a model

Create and manage resources in an Azure Machine Learning workspace

• Create and manage a workspace

• Create and manage datastores

• Create and manage compute targets

• Set up Git integration for source control

Create and manage assets in an Azure Machine Learning workspace

• Create and manage data assets

• Create and manage environments

• Share assets across workspaces by using registries

Explore data, and run experiments (20–25%)

Use automated machine learning to explore optimal models

• Use automated machine learning for tabular data

• Use automated machine learning for computer vision

• Use automated machine learning for natural language processing

• Select and understand training options, including preprocessing and algorithms

• Evaluate an automated machine learning run, including responsible AI guidelines

Use notebooks for custom model training

• Use the terminal to configure a compute instance

• Access and wrangle data in notebooks

• Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute

• Retrieve features from a feature store to train a model

• Track model training by using MLflow

• Evaluate a model, including responsible AI guidelines

Automate hyperparameter tuning

• Select a sampling method

• Define the search space

• Define the primary metric

• Define early termination options

Train and deploy models (25–30%)

Run model training scripts

• Consume data in a job

• Configure compute for a job run

• Configure an environment for a job run

• Track model training with MLflow in a job run

• Define parameters for a job

• Run a script as a job

• Use logs to troubleshoot job run errors

Implement training pipelines

• Create custom components

• Create a pipeline

• Pass data between steps in a pipeline

• Run and schedule a pipeline

• Monitor and troubleshoot pipeline runs

Manage models

• Define the signature in the MLmodel file

• Package a feature retrieval specification with the model artifact

• Register an MLflow model

• Assess a model by using responsible AI principles

Deploy a model

• Configure settings for online deployment

• Deploy a model to an online endpoint

• Test an online deployed service

• Configure compute for a batch deployment

• Deploy a model to a batch endpoint

• Invoke the batch endpoint to start a batch scoring job

Optimize language models for AI applications (25–30%)

Prepare for model optimization

• Select and deploy a language model from the model catalog

• Compare language models using benchmarks

• Test a deployed language model in the playground

• Select an optimization approach

Optimize through prompt engineering and prompt flow

• Test prompts with manual evaluation

• Define and track prompt variants

• Create prompt templates

• Define chaining logic with the prompt flow SDK

• Use tracing to evaluate your flow

Optimize through Retrieval Augmented Generation (RAG)

• Prepare data for RAG, including cleaning, chunking, and embedding

• Configure a vector store

• Configure an Azure AI Search-based index store

• Evaluate your RAG solution

Optimize through fine-tuning

• Prepare data for fine-tuning

• Select an appropriate base model

• Run a fine-tuning job

• Evaluate your fine-tuned model

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