Microsoft MB-340 : Microsoft Dynamics 365 Commerce Functional Consultant Exam Dumps

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Latest 2023 Updated Microsoft Microsoft Dynamics 365 Commerce Functional Consultant Syllabus
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Exam Number : MB-340
Exam Name : Microsoft Dynamics 365 Commerce Functional Consultant
Vendor Name : Microsoft
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MB-340 Exam Format | MB-340 Course Contents | MB-340 Course Outline | MB-340 Exam Syllabus | MB-340 Exam Objectives

Exam Number: MB-340

Exam Name : Microsoft Dynamics 365 Commerce Functional Consultant

Exam TOPICS

Candidates for this exam set up and use the application functionality in Dynamics 365 Commerce and provide support for the application.

Candidates have a strong understanding of unified commerce business operations. They may have experience configuring, deploying, and maintaining Dynamics 365 Commerce.

Configure Dynamics 365 Commerce Headquarters (20-25%)

Configure products, prices, discounts, loyalty, and affiliations (20-25%)

Manage Point of Sales (POS) in Dynamics 365 Commerce (15-20%)

Configure and manage Dynamics 365 Commerce call centers (10-15%)

Manage e-commerce (15-20%)

Configure Dynamics 365 Commerce Headquarters (25-30%)

Configure prerequisites and commerce parameters

 create employee and customer address books

 configure and manage retail workers

 assign address books to customers, channels, and workers

 create email templates and email notification profiles

 configure organizational hierarchies and hierarchy purposes

 configure Commerce shared parameters

 configure company-specific Commerce parameters

Describe and configure additional functionality

 create and configure channel and sales order attributes

 configure commissions and sales representatives

 configure payment methods and card types

 configure and manage gift cards

 describe Omni-channel capabilities including payments, orders, and returns

 configure data distribution

 create info codes, sub-codes, and info code groups

 describe Dynamics 365 Fraud Protection purchase protection, loss prevention, and account protection

Manage statements

 describe advantages of using trickle feed-based posting

 validate retail transactions by using the transaction consistency checker

 configure and manage retail statement calculations and posting

 troubleshoot statement posting issues

Configure Distributed Order Management (DOM)

 configure fulfillment profiles

 configure cost components including shipping, handling, and packaging costs

 configure management rules and parameters

 monitor fulfillment plans and order exceptions

Configure order fulfillment

 configure modes of delivery including shipments, pick up, and carry out

 configure curbside customer order pickup

 configure charge codes, charge groups, and automatic charges

 configure and assign order fulfillment groups

Configure products, prices, discounts, loyalty, and affiliations (25-30%)

Configure products and merchandising

 configure product category hierarchies

 configure product attributes and attribute groups

 configure assortments and product catalogs

 manage product labels and shelf labels

 describe uses cases for recommendation types including product, personalized, Shop

similar looks, and Shop similar descriptions recommendations

 configure recommendations

 configure warranty settings

 configure inventory buffers and inventory levels

 configure products and variants including configuring barcodes

Manage pricing

 design and create price groups

 configure pricing priorities

 configure product pricing including smart rounding

 configure catalog pricing

 configure affiliation pricing

 configure category pricing rules

Manage discounts and promotions

 configure discount parameters

 configure channel or customer-specific discounts

 configure quantity, shipping, tender-based, and threshold-based discounts

 configure discount concurrency rules

 manage coupons

Manage customers, loyalty, and affiliations

 configure client books

 configure customer attributes

 configure customer affiliations

 configure loyalty programs, loyalty schemes, and reward points

 manage loyalty tier calculations and processing

Manage Point of Sale (POS) in Dynamics 365 Commerce (15-20%)

Configure retail stores

 create a retail store

 configure POS registers and devices

 configure retail profiles

 configure sales tax overrides

 configure Task Management lists and parameters

 define cash management processes

 define shifts and shift management processes

 configure channel return policies

 describe offline capabilities and limitations

Manage store inventory

 configure availability calculations for products

 manage inbound and outbound inventory operations

 process customer pick-up and shipment orders

 manage inventory processes including stock counts

 look up product inventory

 process serialized items

Perform POS operations

 perform sales and order processes

 perform end of day processes

 reconcile store cash

 monitor store productivity by using task management and reporting features

Configure and Manage Dynamics 365 Commerce call centers (10-15%)

Configure call centers

 create a call center

 configure and publish product catalogs

 create product catalog scripts

 configure fraud conditions, rules, and variables to trigger order holds

 configure fraud alerts

Configure continuity orders and installment billing

 set up continuity programs and parameters

 configure continuity order batch jobs

 manage continuity child orders

Manage call centers

 create, modify, and process sales orders

 process call center payments

 manage order holds

 create return merchandise authorizations (RMAs)

 process returns, exchanges, and replacements

Manage e-commerce (15-20%)

Configure an e-commerce channel

 create an online store

 configure an e-commerce site

 configure channel assignments for an e-commerce site

 configure ratings and reviews

Manage e-commerce content

 configure URLs and aliases

 configure product detail pages and category pages

 manage site themes, page fragments, templates, layouts, and pages

 upload and manage digital assets including videos and images

 set focal points and attribute values for media assets

 configure publish groups

Operate an e-commerce channel

 create e-commerce orders

 synchronize e-commerce orders

 moderate ratings and reviews

Configure business-to-business (B2B) e-commerce

 describe differences between B2B and business-to-consumer (B2C) solutions

 describe use cases for organizational modeling hierarchies

 manage business partners and business partner users

 configure product quantity limits

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Microsoft Dynamics Latest Questions

From AI to ML: Big Techs and Their Obsessions

The big tech companies are currently driving an incredible amount of innovation and development, making it difficult to keep up with the constant stream of new models and technologies. These companies frequently host major events to showcase their latest advancements.

Apple recently concluded its highly anticipated WWDC 23 event, while Microsoft wrapped up its Build 2023 event. Google also held its Google IO 2023 in March. These events serve as platforms for top AI executives to release a range of new products.

One noticeable trend is the rise of generative AI, which has captivated the attention of the community. People who were previously unfamiliar with AI and machine learning now have an increasing interest in these technologies, thanks to the wave of generative AI.

The big tech companies have all heavily invested in generative AI and AI/ML, shifting their focus from other deep learning methods. Noteworthy developments include Google AI, Microsoft Copilot, Apple Machine Learning, and OpenAI’s pursuit of AGI (Artificial General Intelligence).

Apple’s Machine Learning

Apple has made significant investments in machine learning research, assembling a talented team of researchers and engineers. They have applied machine learning to various projects, including Siri, Photos, Health, and CarPlay, enhancing user experiences. Apple’s long-term commitment to machine learning is evident in its Machine Learning Research Residency Program, which offers training to early-career researchers.

The company’s love for Machine Learning was also very evident at its recent WWDC 2023 event, as it steered clear from parroting the term ‘AI’ unlike all its competitors including Google.

In 2023, Apple introduced new machine learning-based features like Live Text, Visual Look Up, and Safety Check in iOS 16. These initiatives demonstrate Apple’s dedication to leveraging machine learning to transform user interactions and improve their products and services. Expect Apple to continue investing in machine learning research and developing new machine learning-driven offerings in the future.

Apple CEO Tim Cook has also positioned the company as uninterested in collecting user data, which he believes sets Apple apart from companies like Google and Facebook. However, this aversion to cloud computing poses a challenge as Apple seeks to develop new machine learning and AI-powered features. Building and running machine learning services require computing power and data, both of which are more readily available in the cloud. While Apple’s mobile devices have impressive capabilities, they may struggle to compete with servers, especially those equipped with Google’s custom machine learning chips.

GoogleAI

Google has been a significant player in AI research and development, with initiatives like Google Brain and programs such as the Google AI Residency Program. The company has made groundbreaking advancements in AI algorithms and systems, leading to the creation of AI-powered products and services like Google Search, Google Translate, and Google Photos. Google actively conducts and publishes AI research findings and invests in the potential of AI to address global challenges.

However, Google is now facing competition from OpenAI and Microsoft, particularly in the field of generative AI. At Google I/O, the focus was on Bard, a chatbot aimed at competing with OpenAI’s ChatGPT. Some experts feel that Google’s recent approach has been reactive and divergent from its innovation-focused past. The company has shifted its AI operations to prioritize quick product launches, which has led to concerns about neglecting its AI history and potentially falling behind in the market.

Google’s parent company, Alphabet, has been investing in AI for years and acquired DeepMind in 2014. Recently, Alphabet merged its Google Research team with DeepMind to consolidate AI efforts. However, some experts believe that this consolidation should have been done earlier, as Google experienced a “Kodak moment” by not capitalising on its leading AI product, falling behind Microsoft in 2022.

To strengthen its AI focus, Google has made investments in companies like Anthropic, showing its commitment to advancing AI technology. While Google’s prior investments and strong AI technology remain relevant, the company is working to catch up with competitors and bring AI into its products more quickly, similar to the strides made by Microsoft.

Microsoft and Copilot

Microsoft has invested heavily in artificial intelligence in recent years, and its Copilot project is one of the most ambitious examples of this investment. It’s a powerful language model that generates text, translates languages, and assists with various creative tasks. Copilot aims to transform how people work and create by enhancing productivity, fostering creativity, and promoting inclusivity. Microsoft plans to offer Copilot as a free service for Microsoft 365 subscribers and as a standalone product. The tool has the potential to revolutionise AI’s impact on the world, with benefits including increased productivity, improved quality, and expanded creativity. Microsoft has also expanded Copilot’s application in CRM and ERP with Dynamics 365 Copilot, and GitHub has launched Github Copilot for Business, an AI coding assistant for public use.

OpenAGI

OpenAI’s CEO, Sam Altman, and other founders have discussed artificial general intelligence (AGI) on various platforms, expressing both optimism about its potential benefits and concern for its risks. Altman stated in an interview with Lex Fridman that he believes AGI is “probably 10 to 20 years away” and could have a “positive impact on humanity,” emphasising the need to ensure its responsible use.

During his ongoing India visit as well, Altman is discussing AGI. He sees AGI as 10 to 20 years away with the potential to solve global problems. Altman acknowledges AGI risks, including misuse and job displacement. He believes India’s talent and population make it a potential AGI leader. Altman emphasises the importance of considering AGI’s risks and benefits now. He works on safety guidelines and builds an expert community for responsible AGI use. Altman’s visit reflects the growing interest in AGI worldwide. It’s crucial to contemplate AGI’s potential benefits and risks as it becomes more realistic. OpenAI, under Altman’s leadership, focuses on safe and ethical AGI development.

In a blog post, Altman and the other founders outlined their vision for AGI, stating that it could “solve some of the world’s most pressing problems” such as climate change, poverty, and disease, while also fostering human creativity and ingenuity.

However, they acknowledged the potential risks of AGI, including malicious use for creating autonomous weapons or causing mass unemployment by replacing human jobs.

Amazon and Cloud Services

Amazon has invested heavily in AI research and their cloud services serve as a prominent platform for AI development and deployment. Their AI research team focuses on enhancing the performance of cloud services through the development of new AI technologies.

Research areas include machine learning (ML), with a focus on algorithms and models for training and deploying ML models. This research improves the performance of Amazon’s cloud services like SageMaker, Forecast, and Personalise. Additionally, Amazon’s AI research team is dedicated to developing tools and resources for AI developers, which are accessible through their AI research website.

The cloud-based platform SageMaker enables the building, training, and deployment of ML models for various applications such as fraud detection, customer churn prediction, and product recommendations. Amazon’s AI research efforts are advancing the capabilities and versatility of their cloud, opening up new possibilities for businesses and developers to leverage AI in their operations and products.

The recently released Falcon 40B, a large language model, is developed on Amazon Web Services (AWS). The Falcon 40B is a versatile and robust tool for translation, question answering, summarisation, and image identification and is accessible on AWS through Amazon SageMaker JumpStart.

Meta and Self-Supervised Learning

Meta began its SSL journey in 2017, exploring its potential to improve machine learning performance. They developed SSL methods like SimCLR, SwAV, and DINO, achieving state-of-the-art results in tasks like image classification and object detection. Meta invested in large-scale compute clusters, enabling the training of significantly larger SSL models. This progress has had a major impact on AI, with SSL widely used and considered a promising approach. Key milestones include the introduction of SimCLR in 2018, SwAV in 2019, and DINO in 2020. Meta built Megatron, a compute cluster for SSL training, in 2021. In 2022, they published the Data2vec paper, introducing an SSL algorithm across speech, vision, and text modalities. Meta’s continued investment in SSL research will lead to further advances.

As Meta’s VP & Chief AI Scientist Yann LeCunn has emphasised repeatedly, he doesn’t believe in RLHF and thinks it is, “I think RLHF is hopeless because the space of wrong answers is very large, and the space of tricky questions has a very long tail. Any system that does not experience the world and learn for itself is going to be at the mercy of the data it is given to learn from.”

Microsoft Dynamics AX

A comprehensive set of business applications from Microsoft created by Danish-based Damgaard Data A/S under the Axapta brand and acquired by Microsoft in 2002. Introduced in Denmark and the U.S. in 1998, Dynamics AX (Axapta) includes its own development system, which consists of the MorphX environment and X++ programming language. The traditional offerings include CRM, financials (general ledger, A/R, A/P, etc.), supply chain (inventory), human resources, etc., while Dynamics AX 2009 added shop floor control and cost accounting among others.

In 1983, Damgaard Data was founded by brothers Preben and Erik Damgaard. The company merged with Navison Software A/S in 2000 and was renamed NavisonDamgaard and later Navison A/S. In 2002, Microsoft acquired Navison, and thus the Axapta software. See Microsoft Dynamics.

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