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SalesForce Marketing-Cloud-Consultant : Salesforce Certified Marketing Cloud Administrator Exam Dumps

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Exam Number : Marketing-Cloud-Consultant
Exam Name : Salesforce Certified Marketing Cloud Administrator
Vendor Name : SalesForce
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Marketing-Cloud-Consultant Exam Format | Marketing-Cloud-Consultant Course Contents | Marketing-Cloud-Consultant Course Outline | Marketing-Cloud-Consultant Exam Syllabus | Marketing-Cloud-Consultant Exam Objectives

Exam Details for Marketing-Cloud-Consultant Salesforce Certified Marketing Cloud Administrator:

Exam Specification:
- Number of Questions: The exam typically consists of multiple-choice questions, with a total of approximately 60 questions.
- Time Limit: The total time allocated for the exam is usually 90 minutes.
- Passing Score: The passing score for the exam varies, but it is generally set around 65% or higher.
- Exam Format: The exam is usually conducted in a proctored environment, either in-person or online.

Course Outline:

The Salesforce Certified Marketing Cloud Administrator course covers the following key areas:

1. Marketing Cloud Basics:
- Introduction to Salesforce Marketing Cloud
- Marketing Cloud features and capabilities
- Marketing Cloud connectors and integration options
- Marketing Cloud data model and architecture

2. Administration and Configuration:
- Marketing Cloud setup and configuration
- User and permission management
- Data management and data extensions
- Marketing Cloud connectors and data synchronization
- Email studio configuration and sending emails

3. Journey Builder and Automation:
- Building customer journeys in Journey Builder
- Automation studio and workflow automation
- Triggered sends and email automation
- Audience segmentation and personalization

4. Content Creation and Personalization:
- Content builder and email template creation
- Dynamic content and personalization
- Mobile and SMS message creation
- Landing page creation and management

5. Reporting and Analytics:
- Marketing Cloud reporting and analytics
- Tracking and measuring campaign performance
- Data analysis and segmentation
- A/B testing and optimization

Exam Objectives:

The objectives of the Marketing-Cloud-Consultant exam are to assess the candidate's understanding of the following:

1. Salesforce Marketing Cloud features and capabilities.
2. Administration and configuration of Marketing Cloud.
3. Journey Builder and automation in Marketing Cloud.
4. Content creation and personalization in Marketing Cloud.
5. Reporting and analytics in Marketing Cloud.

Exam Syllabus:

The exam syllabus for Marketing-Cloud-Consultant includes the following topics:

1. Marketing Cloud Basics
2. Administration and Configuration
3. Journey Builder and Automation
4. Content Creation and Personalization
5. Reporting and Analytics

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SalesForce Cloud certification

Report: AI startup Hugging Face hoping to raise millions from Salesforce and other investors

Artificial intelligence startup Hugging Face Inc. is reportedly seeking to raise $200 million in venture capital.

It’s said that Salesforce Inc. will lead the proposed round, which will more than double the privately-held company’s valuation, which currently stands at $2 billion. The money will come from Salesforce’s venture capital arm, Salesforce Ventures, and several other unnamed backers, The Information said in its report. It notes that Salesforce is prepared to pay a steep price to obtain a stake in Hugging Face, which operates a platform that allows companies to store and use AI software.

Hugging Face’s platform is similar in some ways to GitHub, which is used by software developers to host their code, manage training datasets and collaborate with others on it. It currently hosts more than 100,000 open-source AI models, which developers can use as the basis of their AI applications. In addition, developers can submit their own neural networks to the platform.

New York-based Hugging Face operates a number of projects, including the BigScience research initiative that has built what is claimed to be the world’s largest open-source natural language processing model. It also offers paid functionality such as AutoTrain, which helps to automate the task of training AI models, and Inference API, which allows developers to host neural networks without managing the underlying infrastructure. Meanwhile, its Infinity offering is used by developers to increase the speed with which a neural network processes data in production.

The startup has already raised more than $160 million in funding, closing on its most recent, $100 million round in May 2022. Hugging Face said at the time of that round that it counted more than 100,000 companies using its Hugging Face Hub to host their AI projects.

The Information said the proposed round would take Hugging Face’s valuation to $4 billion, which is about 100 times greater than its current annualized revenue. It counts some of the biggest names in the technology industry as its partners and customers. For instance, it has partnered with Amazon Web Services Inc. since 2021, selecting AWS as its preferred public cloud platform.

It also collaborated with ServiceNow Inc. on a generative AI model called StarCoder, which can generate software code in multiple programming languages. Earlier this month, it added Nvidia Corp. to its list of partners, enabling customers to deploy AI models directly onto the Nvidia DGX Cloud supercomputing platform.

Now is an opportune moment for Hugging Face to raise new funds, as there has been an explosion of interest in AI this year thanks to the popularity of OpenAI LP’s ChatGPT. A number of high-profile AI startups have already closed on bumper funding rounds this year, while tech companies of all shapes and sizes are scrambling to add AI features to cash in on the trend or remain relevant.

While OpenAI and ChatGPT have stolen most of the headlines around generative AI this year, many competing models are actively being developed elsewhere by other companies, said Charles King of Pund-IT Inc.

“This is especially true for AI models that are designed to meet enterprises’ substantial requirements around security and replicability,” the analyst added. “Hugging Face’s clear focus on open-source projects and code place it well apart from the multi-billion investment Microsoft made in OpenAI. It’s reasonable to assume that many of the companies currently working with Hugging Face have little desire to become reliant on an AI platform and technology that’s so deeply tied to Microsoft.”

Salesforce is a prime example. The company has invested millions of dollars into its own AI models in recent years as it looks to build on its existing Einstein AI capabilities. Those efforts have borne fruit, with Salesforce introducing a number of new AI features this year. Some of its latest innovations include the new generative AI capabilities in its Sales Cloud and Service Cloud applications, which came after similar updates to Marketing Cloud and Commerce Cloud. By investing in Hugging Face, Salesforce could potentially become a key player in the development of open-source AI technologies.

Holger Mueller of Constellation Research Inc. said Hugging Face has emerged as the AI model repository of choice for many of the tech industry’s biggest companies. “It’s also a key partner for many vendors, so it represents a good investment for Salesforce,” he added. “Salesforce needs access to more AI models, and any deal with Hugging Face will surely benefit its customers, providing them with easier access to one of the world’s largest AI model libraries.”

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Salesforce places guardrails around how customers can use its AI

Dive Brief:

Salesforce updated its AI acceptable use policy to place guardrails around its AI services, according to a document published Wednesday. The update comes amid increased industry scrutiny over generative AI data use.

Customers are not allowed to leverage the company's AI products — or any third-party services linked to Salesforce services — for purposes related to child abuse, deepfakes, prediction of protected categories or automating decisions with legal effects, among other use cases.

"These policy updates allow customers to use Salesforce products with confidence, knowing they and their end users are receiving a truly ethical AI experience from product development to deployment," said Paula Goldman, chief ethical and humane use officer at Salesforce, in a blog post Wednesday.

Dive Insight:

The policy update from Salesforce is the latest effort from the provider to address the risk concerns of would-be enterprise technology adopters.

In June, the company rolled out Einstein GPT Trust Layer, a service designed to let customers access enterprise-ready data security and compliance safeguards while leveraging generative AI tools.

"The GPT Trust Layer gives connected LLMs secure, real-time access to data without the need to move all of your data into the LLM itself," said Marc Benioff, Salesforce chairman and co-CEO, speaking in May during the company's Q1 2024 earnings call. "While they're using the LLMs, the data itself is not moving and being stored in the LLM. That is what our customers want."

As enterprise adoption of generative AI advances, more than half of IT leaders say inaccuracies and cybersecurity are associated risks of the emerging technology, according to a report from QuantumBlack, AI by McKinsey.

Salesforce, with its guidelines clearly stating usage limitations, is showing leadership among the provider ecosystem in terms of responsible AI, according to Juliette Powell and Art Kleiner, professors at New York University.

"On the surface, the new policy may look unenforceable, but it's an addition to Salesforce's Acceptable Use and External Facing Services Policy, which clearly states that violators could lose their Salesforce licenses," the professors said via email. "It will be really interesting to see which companies are targeted first."

The new policy from Salesforce comes as another major provider updated its own terms of service in response to criticisms over data use.

Zoom updated its terms and conditions to clarify the provider would be able to access customer content for safety and legal purposes, but not use any customer data to train third-party or its own AI models.

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