Microsoft DP-100 : Designing and Implementing a Data Science Solution on Azure Exam Dumps

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Exam Number : DP-100
Exam Name : Designing and Implementing a Data Science Solution on Azure
Vendor Name : Microsoft
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DP-100 Exam Format | DP-100 Course Contents | DP-100 Course Outline | DP-100 Exam Syllabus | DP-100 Exam Objectives


Set up an Azure Machine Learning workspace (30-35%)

Create an Azure Machine Learning workspace

• create an Azure Machine Learning workspace

• configure workspace settings

• manage a workspace by using Azure Machine Learning Studio

Manage data objects in an Azure Machine Learning workspace

• register and maintain data stores

• create and manage datasets

Manage experiment compute contexts

• create a compute instance

• determine appropriate compute specifications for a training workload

• create compute targets for experiments and training



Run experiments and train models (25-30%)

Create models by using Azure Machine Learning Designer

• create a training pipeline by using Designer

• ingest data in a Designer pipeline

• use Designer modules to define a pipeline data flow

• use custom code modules in Designer

Run training scripts in an Azure Machine Learning workspace

• create and run an experiment by using the Azure Machine Learning SDK

• consume data from a data store in an experiment by using the Azure Machine Learning

SDK

• consume data from a dataset in an experiment by using the Azure Machine Learning

SDK

• choose an estimator

Generate metrics from an experiment run

• log metrics from an experiment run

• retrieve and view experiment outputs

• use logs to troubleshoot experiment run errors

Automate the model training process

• create a pipeline by using the SDK

• pass data between steps in a pipeline

• run a pipeline

• monitor pipeline runs



Optimize and manage models (20-25%)

Use Automated ML to create optimal models

• use the Automated ML interface in Studio

• use Automated ML from the Azure ML SDK

• select scaling functions and pre-processing options

• determine algorithms to be searched

• define a primary metric

• get data for an Automated ML run

• retrieve the best model

Use Hyperdrive to rune hyperparameters

• select a sampling method

• define the search space

• define the primary metric

• define early termination options

• find the model that has optimal hyperparameter values

Use model explainers to interpret models

• select a model interpreter

• generate feature importance data

Manage models

• register a trained model

• monitor model history

• monitor data drift



Deploy and consume models (20-25%)

Create production compute targets

• consider security for deployed services

• evaluate compute options for deployment

Deploy a model as a service

• configure deployment settings

• consume a deployed service

• troubleshoot deployment container issues

Create a pipeline for batch inferencing

• publish a batch inferencing pipeline

• run a batch inferencing pipeline and obtain outputs

Publish a Designer pipeline as a web service

• create a target compute resource

• configure an Inference pipeline

• consume a deployed endpoint



Set up an Azure Machine Learning workspace (30-35%)

Create an Azure Machine Learning workspace

• create an Azure Machine Learning workspace

• configure workspace settings

• manage a workspace by using Azure Machine Learning sStudio

Manage data objects in an Azure Machine Learning workspace

• register and maintain data stores

• create and manage datasets

Manage experiment compute contexts

• create a compute instance

• determine appropriate compute specifications for a training workload

• create compute targets for experiments and training



Run experiments and train models (25-30%)

Create models by using Azure Machine Learning Designer

• create a training pipeline by using Azure Machine Learning Ddesigner

• ingest data in a Designer designer pipeline

• use Designer designer modules to define a pipeline data flow

• use custom code modules in Designer designer

Run training scripts in an Azure Machine Learning workspace

• create and run an experiment by using the Azure Machine Learning SDK

• consume data from a data store in an experiment by using the Azure Machine Learning

SDK

• consume data from a dataset in an experiment by using the Azure Machine Learning

SDK

• choose an estimator for a training experiment

Generate metrics from an experiment run

• log metrics from an experiment run

• retrieve and view experiment outputs

• use logs to troubleshoot experiment run errors

Automate the model training process

• create a pipeline by using the SDK

• pass data between steps in a pipeline

• run a pipeline

• monitor pipeline runs



Optimize and manage models (20-25%)

Use Automated ML to create optimal models

• use the Automated ML interface in Azure Machine Learning Studiostudio

• use Automated ML from the Azure Machine Learning SDK

• select scaling functions and pre-processing options

• determine algorithms to be searched

• define a primary metric

• get data for an Automated ML run

• retrieve the best model

Use Hyperdrive to rune tune hyperparameters

• select a sampling method

• define the search space

• define the primary metric

• define early termination options

• find the model that has optimal hyperparameter values

Use model explainers to interpret models

• select a model interpreter

• generate feature importance data

Manage models

• register a trained model

• monitor model history

• monitor data drift



Deploy and consume models (20-25%)

Create production compute targets

• consider security for deployed services

• evaluate compute options for deployment

Deploy a model as a service

• configure deployment settings

• consume a deployed service

• troubleshoot deployment container issues

Create a pipeline for batch inferencing

• publish a batch inferencing pipeline

• run a batch inferencing pipeline and obtain outputs

Publish a Designer designer pipeline as a web service

• create a target compute resource

• configure an Inference pipeline

• consume a deployed endpoint



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Microsoft Data test

 

The Times Sues OpenAI and Microsoft Over A.I. Use of Copyrighted Work

The New York Times sued OpenAI and Microsoft for copyright infringement on Wednesday, opening a new front in the increasingly intense legal battle over the unauthorized use of published work to train artificial intelligence technologies.

The Times is the first major American media organization to sue the companies, the creators of ChatGPT and other popular A.I. platforms, over copyright issues associated with its written works. The lawsuit, filed in Federal District Court in Manhattan, contends that millions of articles published by The Times were used to train automated chatbots that now compete with the news outlet as a source of reliable information.

The suit does not include an exact monetary demand. But it says the defendants should be held responsible for “billions of dollars in statutory and actual damages” related to the “unlawful copying and use of The Times’s uniquely valuable works.” It also calls for the companies to destroy any chatbot models and training data that use copyrighted material from The Times.

In its complaint, The Times said it approached Microsoft and OpenAI in April to raise concerns about the use of its intellectual property and explore “an amicable resolution,” possibly involving a commercial agreement and “technological guardrails” around generative A.I. products. But it said the talks had not produced a resolution.

An OpenAI spokeswoman, Lindsey Held, said in a statement that the company had been “moving forward constructively” in conversations with The Times and that it was “surprised and disappointed” by the lawsuit.

“We respect the rights of content creators and owners and are committed to working with them to ensure they benefit from A.I. technology and new revenue models,” Ms. Held said. “We’re hopeful that we will find a mutually beneficial way to work together, as we are doing with many other publishers.”

Microsoft declined to comment on the case.

The lawsuit could test the emerging legal contours of generative A.I. technologies — so called for the text, images and other content they can create after learning from large data sets — and could carry major implications for the news industry. The Times is among a small number of outlets that have built successful business models from online journalism, but dozens of newspapers and magazines have been hobbled by readers’ migration to the internet.

At the same time, OpenAI and other A.I. tech firms — which use a wide variety of online texts, from newspaper articles to poems to screenplays, to train chatbots — are attracting billions of dollars in funding.

OpenAI is now valued by investors at more than $80 billion. Microsoft has committed $13 billion to OpenAI and has incorporated the company’s technology into its Bing search engine.

“Defendants seek to free-ride on The Times’s massive investment in its journalism,” the complaint says, accusing OpenAI and Microsoft of “using The Times’s content without payment to create products that substitute for The Times and steal audiences away from it.”

The defendants have not had an opportunity to respond in court.

Concerns about the uncompensated use of intellectual property by A.I. systems have coursed through creative industries, given the technology’s ability to mimic natural language and generate sophisticated written responses to virtually any prompt.

The actress Sarah Silverman joined a pair of lawsuits in July that accused Meta and OpenAI of having “ingested” her memoir as a training text for A.I. programs. Novelists expressed alarm when it was revealed that A.I. systems had absorbed tens of thousands of books, leading to a lawsuit by authors including Jonathan Franzen and John Grisham. Getty Images, the photography syndicate, sued one A.I. company that generates images based on written prompts, saying the platform relies on unauthorized use of Getty’s copyrighted visual materials.

The boundaries of copyright law often get new scrutiny at moments of technological change — like the advent of broadcast radio or digital file-sharing programs like Napster — and the use of artificial intelligence is emerging as the latest frontier.

“A Supreme Court decision is essentially inevitable,” Richard Tofel, a former president of the nonprofit newsroom ProPublica and a consultant to the news business, said of the latest flurry of lawsuits. “Some of the publishers will settle for some period of time — including still possibly The Times — but enough publishers won’t that this novel and crucial issue of copyright law will need to be resolved.”

Microsoft has previously acknowledged potential copyright concerns over its A.I. products. In September, the company announced that if customers using its A.I. tools were hit with copyright complaints, it would indemnify them and cover the associated legal costs.

Other voices in the technology industry have been more steadfast in their approach to copyright. In October, Andreessen Horowitz, a venture capital firm and early backer of OpenAI, wrote in comments to the U.S. Copyright Office that exposing A.I. companies to copyright liability would “either kill or significantly hamper their development.”

“The result will be far less competition, far less innovation and very likely the loss of the United States’ position as the leader in global A.I. development,” the investment firm said in its statement.

Besides seeking to protect intellectual property, the lawsuit by The Times casts ChatGPT and other A.I. systems as potential competitors in the news business. When chatbots are asked about current events or other newsworthy topics, they can generate answers that rely on journalism by The Times. The newspaper expresses concern that readers will be satisfied with a response from a chatbot and decline to visit The Times’s website, thus reducing web traffic that can be translated into advertising and subscription revenue.

The complaint cites several examples when a chatbot provided users with near-verbatim excerpts from Times articles that would otherwise require a paid subscription to view. It asserts that OpenAI and Microsoft placed particular emphasis on the use of Times journalism in training their A.I. programs because of the perceived reliability and accuracy of the material.

Media organizations have spent the past year examining the legal, financial and journalistic implications of the boom in generative A.I. Some news outlets have already reached agreements for the use of their journalism: The Associated Press struck a licensing deal in July with OpenAI, and Axel Springer, the German publisher that owns Politico and Business Insider, did likewise this month. Terms for those agreements were not disclosed.

The Times is exploring how to use the nascent technology itself. The newspaper recently hired an editorial director of artificial intelligence initiatives to establish protocols for the newsroom’s use of A.I. and examine ways to integrate the technology into the company’s journalism.

In one example of how A.I. systems use The Times’s material, the suit showed that Browse With Bing, a Microsoft search feature powered by ChatGPT, reproduced almost verbatim results from Wirecutter, The Times’s product review site. The text results from Bing, however, did not link to the Wirecutter article, and they stripped away the referral links in the text that Wirecutter uses to generate commissions from sales based on its recommendations.

“Decreased traffic to Wirecutter articles and, in turn, decreased traffic to affiliate links subsequently lead to a loss of revenue for Wirecutter,” the complaint states.

The lawsuit also highlights the potential damage to The Times’s brand through so-called A.I. “hallucinations,” a phenomenon in which chatbots insert false information that is then wrongly attributed to a source. The complaint cites several cases in which Microsoft’s Bing Chat provided incorrect information that was said to have come from The Times, including results for “the 15 most heart-healthy foods,” 12 of which were not mentioned in an article by the paper.

“If The Times and other news organizations cannot produce and protect their independent journalism, there will be a vacuum that no computer or artificial intelligence can fill,” the complaint reads. It adds, “Less journalism will be produced, and the cost to society will be enormous.”

The Times has retained the law firms Susman Godfrey and Rothwell, Figg, Ernst & Manbeck as outside counsel for the litigation. Susman represented Dominion Voting Systems in its defamation case against Fox News, which resulted in a $787.5 million settlement in April. Susman also filed a proposed class action suit last month against Microsoft and OpenAI on behalf of nonfiction authors whose books and other copyrighted material were used to train the companies’ chatbots.

Benjamin Mullin contributed reporting.


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    The New York Times is suing OpenAI and Microsoft over ChatGPT training data

    ChatGPT is still the fastest-growing consumer technology product of all time, but it achieved that in large part by crawling the internet and training itself on the backs of every website it could get its hands on. Is that okay? The New York Times thinks not.

    Today, the publication announced that it is suing OpenAI and Microsoft for using its articles to train large language models like GPT-4, which currently power products like ChatGPT. According to the lawsuit, the New York Times claims that the company used “millions” of its articles to “train automated chatbots that now compete with the news outlet as a source of reliable information.”

    The lawsuit demands that the companies “destroy any chatbot models and training data that use copyrighted material from The Times.” I doubt that will happen — removing their content from web crawlers is more likely.

    The suit does not include an exact monetary demand. But it says the defendants should be held responsible for “billions of dollars in statutory and actual damages” related to the “unlawful copying and use of The Times’s uniquely valuable works.” It also calls for the companies to destroy any chatbot models and training data that use copyrighted material from The Times.

    The lawsuit could test the emerging legal contours of generative A.I. technologies — so called for the text, images and other content they can create after learning from large data sets — and could carry major implications for the news industry. The Times is among a small number of outlets that have built successful business models from online journalism, but dozens of newspapers and magazines have been hobbled by readers’ migration to the internet.

    In fact, the outlet has already blocked OpenAI’s web crawlers from using its website to train its models, joining other publications that are also doing so. Other publications like Business Insider have actually signed deals with OpenAI, where the artificial intelligence technology company pays to use the publication’s data.

    Tech. Entertainment. Science. Your inbox.

    The lawsuit comes a month after Sam Altman was fired as OpenAI’s CEO and, in one of the strangest corporate shakeups of all time, returned as CEO just days later. Two weeks ago, we also started to get our first sense of what its latest language model, GPT 4.5, might look like.


     




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