Exam Code: MB-340 Practice exam 2023 by Killexams.com team
MB-340 Microsoft Dynamics 365 Commerce Functional Consultant

Exam Number: MB-340
Exam Name : Microsoft Dynamics 365 Commerce Functional Consultant


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

Microsoft Dynamics 365 Commerce Functional Consultant
Microsoft Functional mock
Killexams : Microsoft Functional mock - BingNews https://killexams.com/pass4sure/exam-detail/MB-340 Search results Killexams : Microsoft Functional mock - BingNews https://killexams.com/pass4sure/exam-detail/MB-340 https://killexams.com/exam_list/Microsoft Killexams : Mock Drafts: For Entertainment Only No result found, try new keyword!Mock drafts are all the rage this time of year, and I'd be the first to admit they are entertaining. However, that's all they are, entertaining because no one outside the walls of the headquarters ... Fri, 17 Feb 2023 02:14:00 -0600 text/html https://www.si.com/nfl/titans/draft/-mock-drafts-for-entertainment-only Killexams : Generative AI is here, along with critical legal implications

Check out all the on-demand sessions from the Intelligent Security Summit here.

Artificial intelligence (AI) has already made its way into our personal and professional lives. Although the term is frequently used to describe a wide range of advanced computer processes, AI is best understood as a computer system or technological process that is capable of simulating human intelligence or learning to perform tasks and calculations and engage in decision-making.

Until recently, the traditional understanding of AI described machine learning (ML) technologies that recognized patterns and/or predicted behavior or preferences (also known as analytical AI). 

Recently, a different kind of AI is revolutionizing the creative process — generative artificial intelligence (GAI). GAI creates content — including images, video and text — from inputs such as text or audio.

For example, we created the image below using the text prompt “lawyers attempting to understand generative artificial intelligence” with DALL·E 2, a text-to-image GAI.


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Image generated by author using DALL·E 2

GAI proponents tout its tremendous promise as a creative and functional tool for an entire range of commercial and noncommercial purposes for industries and businesses of all stripes. This may include filmmakers, artists, Internet and digital service providers (ISPs and DSPs), celebrities and influencers, graphic designers and architects, consumers, advertisers and GAI companies themselves.

With that promise comes a number of legal implications. For example, what rights and permissions are implicated when a GAI user creates an expressive work based on inputs involving a celebrity’s name, a brand, artwork, and potentially obscene, defamatory or harassing material? What might the creator do with such a work, and how might such use impact the creator’s own legal rights and the rights of others?

This article considers questions like these and the existing legal frameworks relevant to GAI stakeholders.

GAIs, like other AI, learn from data training sets according to parameters set by the AI programmer. A text-to-image GAI — such as OpenAI’s DALL·E 2 or Stability AI’s Stable Diffusion — requires access to a massive library of images and text pairs to learn concepts and principles.

Similar to how humans learn to associate a blue sky with daytime, GAI learns this through data sets, then processes a photograph of a blue sky with the associated text “day” or “daytime.” From these training sets, GAIs quickly yield unique outputs (including images, videos or narrative text) that might take a human operator significantly more time to create.

For example, Stability AI has stated that its current GAI “model learns from principles, so the outputs are not direct replicas of any single piece.”

The starting data sets implementing software code and expressive outputs raise legal questions. These include important issues of copyright, trademark, right of publicity, privacy and expressive rights under the First Amendment.

For example, depending on how they are coded, these training sets may include copyrighted images that could be incorporated into the GAI’s process without the permission of the copyright owner — indeed, this is squarely at issue in a recently filed class action lawsuit against Stability AI, Midjourney and DeviantArt.

Or they may include images or likenesses of celebrities, politicians or private figures used in ways that may violate those individuals’ right of publicity or privacy rights in the U.S. or abroad. Is allowing users to prompt a GAI to create an image “in the style” of someone permissible if it might dilute the market for that individual’s work? And what if GAIs render outputs that incorporate registered trademarks or suggest product endorsements? The numerous potential permutations of inputs and outputs provide rise to a diverse range of legal issues. 

Several leaders in GAI development have begun thinking about or implementing collaborative solutions to address these concerns. For example, OpenAI and Shutterstock recently announced a deal whereby OpenAI will pay for the use of stock images owned by Shutterstock, which in turn “will reimburse creators when the company sells work to train text-to-image AI models.” For its part, Shutterstock agreed to exclusively purchase GAI-generated content produced with OpenAI.

As another example, Stability AI has stated that it may allow creators to choose whether their images will be part of the GAI data sets in the future. 

Education essential

Other potential copyright risks include both claims against GAI users for direct infringement and against GAI platforms for secondary (contributory or vicarious) infringement. Whether or not such claims might succeed, copyright stakeholders are likely to be closely watching the GAI industry, and the novelty and complexity of the technology are sure to present issues of first impression for litigants and courts. 

Indeed, appropriately educating courts about how GAIs work in practice, the differences between GAI engines and the relevant terminology will be critical to litigating claims in this space. For example, the process of “diffusion” that is central to current GAIs typically includes deconstructing images and inputs and repeatedly refining, retooling and rebuilding pixels until a particular output sufficiently correlates to the prompts provided.

Given how the original inputs are broken down and reconstituted, one might even compare the diffusion process to the transformation a caterpillar undergoes in its chrysalis to become a butterfly. On the other hand, litigants challenging GAI platforms have asserted that “AI image generators are 21st-century collage tools that violate the rights of millions of artists.”

When stakeholders, litigants, and courts understand the nuances of the processes involved, they will better be able to reach results that are consistent with the legal frameworks at play.

Is a GAI-created work a transformative fair use?

While some GAI platforms are taking steps to address concerns regarding the use of copyrighted material as inputs and their inclusion in and effect on creative outputs, the fair use doctrine will surely have a role to play for GAI stakeholders as both potential plaintiffs and defendants.

In particular, given the nature of GAI, questions about “transformativeness” are likely to predominate. The more a GAI “transforms” copyrighted images, text or other protected inputs, the more likely owners of GAI platforms and their users are to assert that the use of or reference to copyrighted material is a non-actionable fair use or protected by the First Amendment. 

The traditional four fair use factors will guide courts’ determinations of whether particular GAI-created works qualify for fair use protection. This includes the “purpose and character of the use, including whether such use is of a commercial nature.” Also, “the nature of the underlying copyrighted work itself,” the “amount and substantiality of the portion used in relation to the copyrighted work as a whole,” and “the effect of the use upon the potential market for or value of the copyrighted work.” (17 U.S.C. § 107). 

The fair use doctrine is currently before the Supreme Court in Andy Warhol Found. for Visual Arts, Inc. v. Goldsmith, 11 F.4th 26 (2d Cir. 2021), cert. granted, ___ U.S. ___, 142 S. Ct. 1412 (2022), and the Court’s ruling is highly likely to impact how stakeholders across creative industries (including GAI stakeholders) operate and whether constraints on the fair use framework around copyright will be loosened or tightened (or otherwise affected).

Lawsuits already; more to come

GAI platforms should also consider whether and to what extent the software itself is making a copy of a copyrighted image as part of the GAI process (“cache copying”), even if the output is a significantly transformed version of the inputs.

Doing so as part of the GAI process may provide rise to claims of infringement or might be protected as fair use. As usual, these legal questions are highly fact-dependent, but GAI platforms may be able to limit potential liability depending on how their GAI engines function in practice.

And indeed, on November 3, 2022, unnamed programmers filed a proposed class action complaint against GitHub, Microsoft and OpenAI for allegedly infringing protected software code via Copilot, their AI-based product meant to assist and speed the work done by software coders. In a press release issued in connection with the lawsuit, one of the plaintiffs’ lawyers stated, “As far as we know, this is the first class action case in the U.S. challenging the training and output of AI systems. It will not be the last. AI systems are not exempt from the law.” 

These attorneys fulfilled their prediction when they filed their next lawsuit (referenced above) in January 2023, asserting claims against Stability AI, Midjourney and DeviantArt, including for direct and vicarious copyright infringement, violation of the DMCA and violation of California’s statutory and common law right of publicity. 

The named plaintiffs — three visual artists seeking to represent classes of artists and copyright owners — allege that the generated images “are based entirely on the training images [including their works] and are derivative works of the particular images Stable Diffusion draws from when assembling a given output. Ultimately, it is merely a complex collage tool.”

The defendants are sure to disagree with this characterization, and litigation over the specific technical details of the GAI software is likely to be front and center in this action.

Ownership and licensing of AI-generated content

Ownership of GAI-generated content and what the owner can do with such content raises additional legal issues. As between the GAI platform and the user, the details of ownership and usage rights are likely to be governed by GAI terms of service (TOS) agreements.

For this reason, GAI platforms should carefully consider the language of the TOS, what rights and permissions they purport to grant users, and whether and to what extent the platform can mitigate risk when users exploit content in a manner that might violate the TOS. Currently, TOS provisions regarding who is the owner of GAI output and what they can do with it may differ by platform.

For example, with Midjourney, the user owns the GAI-generated image. However, the company retains a broad perpetual, non-exclusive license to use the GAI-generated image and any text or images the user includes in prompts. However, terms are likely to change and evolve over time, including in reaction to the pace of technological development and ensuing legal developments,  

OpenAI’s current terms provide that “as between the parties and to the extent permitted by applicable law, you own all Input, and subject to your compliance with these Terms, OpenAI hereby assigns to you all its right, title and interest in and to Output.”  

Questions of ownership front and center

As companies continue to consider who should own and control GAI content outputs, they will need to weigh considerations of creative flexibility against potential liabilities and harms, and terms and policies that may evolve over time.

Separate questions of permissible use arise for parties who have licensed content that may be included in training sets or GAI outputs. Such licenses — especially if created before GAI was a potential consideration by the parties to such license agreement — may provide rise to disputes or require renegotiations. The intent of parties to include all potential future technologies, including those unforeseen at the time of contracting, implicates additional legal issues relevant here.

While questions of ownership are front and center, one key player in the GAI process — the AI itself — is unlikely to qualify for ownership anytime soon. Despite the efforts of AI-rights activists, the U.S. Patent and Trademark Office (USPTO), Copyright Office and courts have been broadly in agreement that an AI (as a nonhuman author) cannot itself own the rights in a work the AI creates or facilitates.

This issue merits watching, however; Shira Perlmutter, register of copyrights and director of the U.S. Copyright Office has indicated the intention to closely examine the AI space, including questions of authorship and generative AI. And a lawsuit challenging the denial of registration of an allegedly AI-authored work remains pending before a court in Washington D.C.

Political concerns and potential liability for immoral and illegal GAI-generated images

Apart from concerns of infringement, GAI raises issues about the potential creation and misuse of harmful, abusive or offensive content. Indeed, this has already occurred via the creation of deepfakes, including deep-faked nonconsensual pornography, violent imagery and political misinformation.

These potentially nefarious uses of the technology have caught the attention of lawmakers, including Congresswoman Anna Eshoo, who wrote a letter to the U.S. National Security Advisor and the Office of Science and Technology Policy to highlight the potential for misuse of “unsafe” GAIs and to call for the regulation of these AI models. In particular, Eshoo discussed the release of open-source GAIs, which present different liability issues because users can remove safety filters from the original GAI code. Without these guardrails — or a platform ensuring compliance with TOS standards — a user can leverage the technology to create violent, abusive, harassing or other offensive images. 

In view of the potential abuses and concerns around AI, the White House Office of Science and Technology Policy recently issued its Blueprint for an AI Bill of Rights, which is meant to “help guide the design, development and deployment of AI and other automated systems so that they protect the rights of the American public.” The Blueprint focuses on safety, algorithmic discrimination protections and data privacy, among other principles. In other words, the government is paying attention to the AI industry.

Given the potential for misuse of GAI and the potential for governmental regulation, more mainstream platforms have taken steps to implement mitigation measures.

AI is in its relative infancy, and as the industry expands, governmental regulators and lawmakers as well as litigants are likely to increasingly need to reckon with these technologies.

Nathaniel Bach is a litigation partner at Manatt Entertainment.

Eric Bergner is a partner and leader of Manatt’s Digital and Technology Transactions practice.

Andrea Del-Carmen Gonzalez is a litigation associate at Manatt Entertainment.


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Sat, 18 Feb 2023 09:20:00 -0600 en-US text/html https://venturebeat.com/ai/generative-ai-is-here-along-with-critical-legal-implications/
Killexams : Mock Draft Roundup 4.0: A Surprising Alternative Emerges

RB Bijan Robinson, Texas


Reed Hoffmann/AP Photos

"This might raise some eyebrows, especially with a handful of Day 1 receivers still on the board, but GM Eric DeCosta could add another outside target to pair with Rashod Bateman and Devin Duvernay via free agency and/or on Day 2. Robinson is my No. 9 prospect, pushed down the board by positional value, and the Ravens lean on the run like few other teams. We've now watched them look to free agents and practice-squaders at running back in back-to-back years, as J.K. Dobbins and Gus Edwards (both entering the final years of their deals) navigated various injuries. With Robinson available, coach John Harbaugh could make a splash.

"Robinson reminds me of Saquon Barkley. He forces missed tackles with ease (FBS-leading 91 in 2022), has burst through rushing lanes and can make plays in the pass game. If Lamar Jackson does indeed end up back in Baltimore, this would form a scary rushing unit for new offensive coordinator Todd Monken."

"Bijan and Lamar Jackson in the same backfield is just too much dynamism to pass up. Do you want to take your run game to unstoppable levels? Because adding the PFF College single-season broken tackle leader will do that."

"Bijan Robinson is special, and while he is probably one of the best players in this class, he could still be around late in Round 1 because he's a running back. And while the Ravens and offensive coordinator Greg Roman have parted ways, it's fair to assume that the team will still revolve around Lamar Jackson and the run game. J.K. Dobbins showed flashes after returning from ACL surgery, but there isn't a lot of depth behind him. A backfield of Jackson, Dobbins and Robinson is a problem for the rest of the AFC, even if Baltimore has more pressing needs here."


Sam Hodde/AP Photos

2022 stats: 13 games, 60 catches, 1,069 yards, 6 touchdowns

"Take a second and call up some highlights of Johnson running deep crossers -- they're awesome. His speed on deeper routes is consistent and would create space for guys like tight end Mark Andrews to operate."

"The Ravens were hoping for a second-year breakout from 2021 first-round pick Rashod Bateman in 2022, but that never materialized due to injury as Lamar Jackson threw to Devin Duvernay and Demarcus Robinson much more than they had originally planned. Selecting an athletic playmaker in TCU's Quentin Johnson would add a vertical threat to the wide receiver room that they lost in 2022 when they traded Marquise Brown to Arizona."

"Baltimore's never-ending quest to find a receiver to pair with Lamar Jackson continues with Johnston. He's not the most fluid athlete nor the most polished route runner, but Johnston is a nightmare on slants underneath and can get over the top of a defense. I think he'd pair well with Lamar."

WR Jordan Addison, USC


Mark J. Terrill/AP Photos

"The Ravens have really struggled offensively when Rashod Bateman has been out of the lineup. They need to find another viable receiver for Lamar Jackson. Jordan Addison has game-breaking speed, and he had an incredible 2021 campaign with 17 touchdowns. He's a major reason why Kenny Pickett was drafted in the first round."

CB Cam Smith, South Carolina


Artie Walker Jr./AP Photos

"With Marcus Peters entering free agency, the Ravens can add Smith to play opposite Marlon Humphrey in a division with tons of talented receivers."

CB Deonte Banks, Maryland


Julio Cortez/AP Photos

2022 stats: 12 games, 38 tackles, 1 interception, 8 passes defended

"Baltimore stays in-state to find its boundary cornerback replacement for Marcus Peters. Banks is lower on my personal board, but I understand why a team looking for boundary help would be interested. Once teams get through that initial wave of blue-chip talent, there is a large pool of players who could be justified in the first round."

CB Christian Gonzalez, Oregon

2022 stats: 12 games, 50 tackles, 4 interceptions, 7 passes defensed


Godofredo A. Vásquez/AP Photos

"The Ravens add youth to their secondary with Gonzalez, whose film was super clean in 2022."

WR Jalin Hyatt, Tennessee

2022 stats: 12 games, 67 catches, 1,267 yards, 15 touchdowns


Wade Payne/AP Photos

"For the third time in five years, the Ravens select a receiver in the first round. After trading Marquise Brown (No. 25 overall, 2019) during Thursday night's festivities last year, Baltimore could be looking for a speedster to play with 2021 first-rounder Rashod Bateman (No. 27). Hyatt possesses the pure speed to stretch defenses vertically."

WR Jaxon Smith-Njigba, Ohio State

_2022 stats: 3 games, 5 catches, 43 yards, 0 touchdowns _


Jay LaPrete/AP Photos

Thu, 16 Feb 2023 05:46:00 -0600 en-US text/html https://www.baltimoreravens.com/news/bijan-robinson-ravens-mock-draft-2023-roundup-surprise
Killexams : What is mock trial?

When my friends, family and teachers began asking me “what is mock trial,” I consistently struggled to find the right answer. My standard five word response, “It’s like a fake trial,” didn’t quite seem to cover it.

On a surface level, mock trial is a unique opportunity for high schoolers to get engaged with the legal world. For me, it was the perfect chance to explore a field I had been interested in since childhood.

Every year, our team spent months exploring the 90-page case packets, picking apart witness statements and evidentiary rules down to small semantic differences. During hundreds of hours of practice, those packets became the elements of a real trial: direct and cross examinations, pretrial motions and speeches. In January and February, we finally put that analysis into practice, running the case against other teams in our county in modified trials at local courthouses.

Throughout the pandemic, our competition transitioned to an online format. While those years of competing were still filled with invaluable experiences, they were definitely different from the in-person trials we have returned to this year. It has made students appreciate our in-person competition format. The experience is an incredible one and would not be possible without the legal professionals and teachers who dedicate time to the program (thank you Kevin and Ms. Kalinski!).

Mock trial is a series of well-handled mistakes. It is almost unquestionable that mistakes will occur in each trial, and handling those missteps is the crux of the competition. That aspect of the activity forces participants to take mistakes in stride and learn from them.

I spent the better part of my middle school years refusing to raise my hand, paralyzed by the fear of making a mistake. That mindset carried through to my freshman year, during which I was often hesitant to offer my ideas in class for fear of being wrong. But that was completely different in mock trial. Throughout the year, older students and mentors demonstrated growth through mistakes, and celebrated that process as a mark of effort or success.

Through mock trial, those same people taught me that taking a risk and being incorrect is far more valuable than not speaking up. To paraphrase what my coach told me during my first year, you can be the smartest person in the courtroom, but it doesn’t help anyone if you don’t speak up. Mock trial requires loudly pronouncing your argument to a waiting audience, even if you might be incorrect.

Most importantly, mock trial is a community. Through late-night Zoom calls, arguments over objections and hours of repetitive practice, our team has forged a multilaterally supportive community, without which I never would have been able to take advantage of mock trial’s learning opportunities or experience those lessons.

Beyond just our team, mock trial brings students from all schools and backgrounds together to compete. My teammates and I have created inter-team friendships, allowing for connections between students and schools who care about exploring the law. As competitive as it can be, the energy in the courthouse between trials is one filled with positivity and excitement.

Some of my friends have (lightheartedly) poked fun at mock trial, ironically comparing it to our school’s sports teams because of how seriously students take it. While it definitely isn’t a sport, it’s a unique way to experience those elements of community and collaborative effort.

Each competition is a fake trial, but they are also much more than that. They couldn’t possibly be summarized in five words. They are experience, they are personal growth and they are community.

Ellen Kim is a senior at San Mateo High School. Student News appears in the weekend edition. You can email Student News at news@smdailyjournal.com.

Fri, 10 Feb 2023 22:19:00 -0600 en text/html https://www.smdailyjournal.com/opinion/columnists/what-is-mock-trial/article_3b317614-a9ca-11ed-a0f8-0f68ea336b04.html
Killexams : Fantasy baseball: Head-to-head points mock draft

The ESPN fantasy baseball crew conducted their first fantasy baseball mock draft of the season, using the new default standard head-to-head points league scoring and roster settings.

That meant drafting rosters of 19 players per team (down from 26 in previous years), which put more of the spotlight on star-caliber baseball talents. Our fantasy experts also accounted for ESPN's scoring tweaks for pitchers; wins, previously worth 5 points, are now worth 2 apiece, while losses, previously worth minus-5, are now minus-2 points. Holds joined the fray with a 2-point valuation.

Default rosters now include 16 starters: seven pitchers of any kind along with a C, 1B, 2B, 3B, SS, three outfielders and a utility player (can be any position, and is also the only slot to allow a DH-only player). Bench spots are cut down to three.

Hitters score one point for every base reached via hits (total bases), as well as each walk, run, RBI and stolen base, and lose one point when they strike out. Pitchers earn a point for every out they record (three per inning) and an extra point for a strikeout, as well as five points for a win or a save. Pitchers lose two points per run allowed, one point per baserunner (hit or walk) and five points for a loss.

This draft was held on Monday., Feb. 13 and included Todd Zola, Eric Karabell, Jim McCormick, David Schoenfield, Derek Carty, AJ Mass, Pierre Becquey, Tristan H. Cockcroft, Kyle Soppe and James Best.

If you'd like to conduct your own mock drafts, check out the Mock Draft Lobby, select one of several league types and sizes available, and you'll be mock drafting in minutes. Ready for the real thing? Create or join a fantasy baseball league for free.

Round 1

1. Shohei Ohtani, LAA (DH1) -- Zola
2. Juan Soto, SD (OF1) -- Karabell
3. Jose Ramirez, CLE (3B1) -- McCormick
4. Aaron Judge, NYY (OF2) -- Schoenfield
5. Vladimir Guerrero Jr., TOR (1B1) -- Carty
6. Freddie Freeman, LAD (1B2) -- Mass
7. Gerrit Cole, NYY (SP1) -- Becquey
8. Corbin Burnes, MIL (SP2) -- Cockcroft
9. Mookie Betts, LAD (OF3) -- Soppe
10. Max Scherzer, NYM (SP3) -- Best

Round 2

11. Sandy Alcantara, MIA (SP4) -- Best
12. Manny Machado, SD (3B2) -- Soppe
13. Yordan Alvarez, HOU (OF4) -- Cockcroft
14. Trea Turner, PHI (SS1) -- Becquey
15. Shane Bieber, CLE (SP5) -- Mass
16. Jacob deGrom, TEX (SP6) -- Carty
17. Kyle Tucker, HOU (OF5) -- Schoenfield
18. Aaron Nola, PHI (SP7) -- McCormick
19. Justin Verlander, NYM (SP8) -- Karabell
20. Matt Olson, ATL (1B3) -- Zola

Round 3

21. Kevin Gausman, TOR (SP9) -- Zola
22. Jose Altuve, HOU (2B1) -- Karabell
23. Francisco Lindor, NYM (SS2) -- McCormick
24. Julio Rodriguez, SEA (OF6) -- Schoenfield
25. Carlos Rodon, NYY (SP10) -- Carty
26. Rafael Devers, BOS (3B3) -- Mass
27. Pete Alonso, NYM (1B4) -- Becquey
28. Spencer Strider, ATL (SP11) -- Cockcroft
29. Brandon Woodruff, MIL (SP12) -- Soppe
30. Marcus Semien, TEX (2B2) -- Best

Round 4

31. Corey Seager, TEX (SS3) -- Best
32. Fernando Tatis Jr., SD (SS4) -- Soppe
33. Alex Bregman, HOU (3B4) -- Cockcroft
34. Dylan Cease, CWS (SP13) -- Becquey
35. Bo Bichette, TOR (SS5) -- Mass
36. Luis Castillo, SEA (SP14) -- Carty
37. Shane McClanahan, TB (SP15) -- Schoenfield
38. Mike Trout, LAA (OF7) -- McCormick
39. Paul Goldschmidt, STL (1B5) -- Karabell
40. Nolan Arenado, STL (3B5) -- Zola

Round 5

41. Zack Wheeler, PHI (SP16) -- Zola
42. Austin Riley, ATL (3B6) -- Karabell
43. Ronald Acuna Jr., ATL (OF8) -- McCormick
44. Wander Franco, TB (SS6) -- Schoenfield
45. Kyle Schwarber, PHI (OF9) -- Carty
46. Tyler Glasnow, TB (SP17) -- Mass
47. Will Smith, LAD (C1) -- Becquey
48. Ozzie Albies, ATL (2B3) -- Cockcroft
49. Alek Manoah, TOR (SP18) -- Soppe
50. Jose Abreu, HOU (1B6) -- Best

Round 6

51. Steven Kwan, CLE (OF10) -- Best
52. Robbie Ray, SEA (SP19) -- Soppe
53. Edwin Diaz, NYM (RP1) -- Cockcroft
54. Bobby Witt Jr., KC (SS7) -- Becquey
55. Triston McKenzie, CLE (SP20) -- Mass
56. Bryan Reynolds, PIT (OF11) -- Carty
57. Emmanuel Clase, CLE (RP2) -- Schoenfield
58. Cristian Javier, HOU (SP21) -- McCormick
59. Julio Urias, LAD (SP22) -- Karabell
60. Jake Cronenworth, SD (2B4) -- Zola

Round 7

61. Anthony Santander, BAL (OF12) -- Zola
62. Carlos Correa, MIN (SS8) -- Karabell
63. Max Fried, ATL (SP23) -- McCormick
64. Zac Gallen, ARI (SP24) -- Schoenfield
65. Xander Bogaerts, SD (SS9) -- Carty
66. Michael Harris II, ATL (OF13) -- Mass
67. George Springer, TOR (OF14) -- Becquey
68. Devin Williams, MIL (RP3) -- Cockcroft
69. Joe Musgrove, SD (SP25) -- Soppe
70. Cedric Mullins, BAL (OF15) -- Best

Round 8

71. Gunnar Henderson, BAL (3B7) -- Best
72. Josh Bell, CLE (1B7) -- Soppe
73. Adley Rutschman, BAL (C2) -- Cockcroft
74. Framber Valdez, HOU (SP26) -- Becquey
75. Josh Hader, SD (RP4) -- Mass
76. Raisel Iglesias, ATL (RP5) -- Carty
77. Logan Webb, SF (SP27) -- Schoenfield
78. Luis Arraez, MIA (1B8) -- McCormick
79. Yu Darvish, SD (SP28) -- Karabell
80. Logan Gilbert, SEA (SP29) -- Zola

Round 9

81. Masataka Yoshida, BOS (OF16) -- Zola
82. Corbin Carroll, ARI (OF17) -- Karabell
83. Dansby Swanson, CHC (SS10) -- McCormick
84. Vinnie Pasquantino, KC (1B9) -- Schoenfield
85. Yandy Diaz, TB (3B8) -- Carty
86. Daulton Varsho, TOR (C3) -- Mass
87. Nestor Cortes, NYY (SP30) -- Becquey
88. Brandon Nimmo, NYM (OF18) -- Cockcroft
89. Luis Robert, CWS (OF19) -- Soppe
90. Randy Arozarena, TB (OF20) -- Best

Round 10

91. J.T. Realmuto, PHI (C4) -- Best
92. Eloy Jimenez, CWS (OF21) -- Soppe
93. Felix Bautista, BAL (RP6) -- Cockcroft
94. Kyle Wright, ATL (SP31) -- Becquey
95. Lucas Giolito, CWS (SP32) -- Mass
96. Rhys Hoskins, PHI (1B10) -- Carty
97. George Kirby, SEA (SP33) -- Schoenfield
98. Tommy Edman, STL (2B5) -- McCormick
99. Jordan Romano, TOR (RP7) -- Karabell
100. Kris Bryant, COL (OF22) -- Zola

Round 11

101. Pablo Lopez, MIN (SP34) -- Zola
102. Ryan Pressly, HOU (RP8) -- Karabell
103. Chris Bassitt, TOR (SP35) -- McCormick
104. Jeff McNeil, NYM (2B6) -- Schoenfield
105. Charlie Morton, ATL (SP36) -- Carty
106. Brandon Lowe, TB (2B7) -- Mass
107. Ketel Marte, ARI (2B8) -- Becquey
108. Willy Adames, MIL (SS11) -- Cockcroft
109. Blake Snell, SD (SP37) -- Soppe
110. Salvador Perez, KC (C5) -- Best

Round 12

111. Brady Singer, KC (SP38) -- Best
112. Taylor Ward, LAA (OF23) -- Soppe
113. Ryan Helsley, STL (RP9) -- Cockcroft
114. Luis Garcia, HOU (SP39) -- Becquey
115. Triston Casas, BOS (1B11) -- Mass
116. Luis Severino, NYY (SP40) -- Carty
117. Nathaniel Lowe, TEX (1B12) -- Schoenfield
118. Christian Walker, ARI (1B13) -- McCormick
119. Ty France, SEA (1B14) -- Karabell
120. Oneil Cruz, PIT (SS12) -- Zola

Round 13

121. Jordan Montgomery, STL (SP41) -- Zola
122. Nick Castellanos, PHI (OF24) -- Karabell
123. Kenley Jansen, BOS (RP10) -- McCormick
124. Alejandro Kirk, TOR (C6) -- Schoenfield
125. Max Muncy, LAD (3B9) -- Carty
126. Starling Marte, NYM (OF25) -- Mass
127. MJ Melendez, KC (C7) -- Becquey
128. Anthony Rizzo, NYY (1B15) -- Cockcroft
129. Nick Lodolo, CIN (SP42) -- Soppe
130. Amed Rosario, CLE (SS13) -- Best

Round 14

131. Jose Berrios, TOR (SP43) -- Best
132. Jorge Polanco, MIN (2B9) -- Soppe
133. Teoscar Hernandez, SEA (OF26) -- Cockcroft
134. Alex Verdugo, BOS (OF27) -- Becquey
135. Andrew Vaughn, CWS (OF28) -- Mass
136. Willson Contreras, STL (C8) -- Carty
137. Andres Munoz, SEA (RP11) -- Schoenfield
138. Clayton Kershaw, LAD (SP44) -- McCormick
139. Camilo Doval, SF (RP12) -- Karabell
140. Sean Manaea, SF (SP45) -- Zola

Round 15

141. Keibert Ruiz, WSH (C9) -- Zola
142. Lance Lynn, CWS (SP46) -- Karabell
143. Jeffrey Springs, TB (SP47) -- McCormick
144. Hunter Greene, CIN (SP48) -- Schoenfield
145. Chris Sale, BOS (SP49) -- Carty
146. Lance McCullers Jr., HOU (SP50) -- Mass
147. Adolis Garcia, TEX (OF29) -- Becquey
148. Kodai Senga, NYM (SP51) -- Cockcroft
149. Sean Murphy, ATL (C10) -- Soppe
150. Rowdy Tellez, MIL (1B16) -- Best

Round 16

151. Merrill Kelly, ARI (SP52) -- Best
152. Jon Gray, TEX (SP53) -- Soppe
153. Christian Yelich, MIL (OF30) -- Cockcroft
154. A.J. Minter, ATL (RP13) -- Becquey
155. David Bednar, PIT (RP14) -- Mass
156. Byron Buxton, MIN (OF31) -- Carty
157. Jose Miranda, MIN (1B17) -- Schoenfield
158. Jordan Walker, STL (3B10) -- McCormick
159. Scott Barlow, KC (RP15) -- Karabell
160. Tim Anderson, CWS (SS14) -- Zola

Round 17

161. Jesse Winker, MIL (OF32) -- Zola
162. Freddy Peralta, MIL (SP54) -- Karabell
163. Gleyber Torres, NYY (2B10) -- McCormick
164. Lars Nootbaar, STL (OF33) -- Schoenfield
165. Paul Sewald, SEA (RP16) -- Carty
166. DJ LeMahieu, NYY (3B11) -- Mass
167. Brandon Drury, LAA (3B12) -- Becquey
168. Jesus Luzardo, MIA (SP55) -- Cockcroft
169. Joe Ryan, MIN (SP56) -- Soppe
170. J.D. Martinez, LAD (DH2) -- Best

Round 18

171. Alex Cobb, SF (SP57) -- Best
172. Jazz Chisholm Jr., MIA (2B11) -- Soppe
173. Vaughn Grissom, ATL (2B12) -- Cockcroft
174. Thairo Estrada, SF (2B13) -- Becquey
175. Shea Langeliers, OAK (DH3) -- Mass
176. Clay Holmes, NYY (RP17) -- Carty
177. Evan Phillips, LAD (RP18) -- Schoenfield
178. Oscar Gonzalez, CLE (OF34) -- McCormick
179. Tyler Stephenson, CIN (C11) -- Karabell
180. Tony Gonsolin, LAD (SP58) -- Zola

Round 19

181. Drew Rasmussen, TB (SP59) -- Zola
182. Andrew Painter, PHI (SP60) -- Karabell
183. Danny Jansen, TOR (C12) -- McCormick
184. Andres Gimenez, CLE (2B14) -- Schoenfield
185. Tyler O'Neill, STL (OF35) -- Carty
186. A.J. Puk, MIA (RP19) -- Mass
187. Daniel Bard, COL (RP20) -- Becquey
188. Alexis Diaz, CIN (RP21) -- Cockcroft
189. Jack Flaherty, STL (SP61) -- Soppe
190. Tyler Mahle, MIN (SP62) -- Best

Team rosters are presented in first-round pick order. Primary position is used. If a player qualifies at more than one position, all positions are included in parentheses. Pick is displayed as "Round.Pick".

Team Zola

C1 Keibert Ruiz, WSH (Pick: 15.1)
1B1 Matt Olson, ATL (Pick: 2.10)
3B1 Nolan Arenado, STL (Pick: 4.10)
2B1 Jake Cronenworth, SD (Pick: 6.10 | 2B/1B)
SS1 Oneil Cruz, PIT (Pick: 12.10)
SS2 Tim Anderson, CWS (Pick: 16.10)
OF1 Anthony Santander, BAL (Pick: 7.1)
OF2 Masataka Yoshida, BOS (Pick: 9.1)
OF3 Kris Bryant, COL (Pick: 10.10)
OF4 Jesse Winker, MIL (Pick: 17.1)
DH1 Shohei Ohtani, LAA (Pick: 1.1 | DH/SP)
SP1 Kevin Gausman, TOR (Pick: 3.1)
SP2 Zack Wheeler, PHI (Pick: 5.1)
SP3 Logan Gilbert, SEA (Pick: 8.10)
SP4 Pablo Lopez, MIN (Pick: 11.1)
SP5 Jordan Montgomery, STL (Pick: 13.1)
SP6 Sean Manaea, SF (Pick: 14.10)
SP7 Tony Gonsolin, LAD (Pick: 18.10)
SP8 Drew Rasmussen, TB (Pick: 19.1)

Team Karabell

C1 Tyler Stephenson, CIN (Pick: 18.9)
1B1 Paul Goldschmidt, STL (Pick: 4.9)
1B2 Ty France, SEA (Pick: 12.9)
3B1 Austin Riley, ATL (Pick: 5.2)
2B1 Jose Altuve, HOU (Pick: 3.2)
SS1 Carlos Correa, MIN (Pick: 7.2)
OF1 Juan Soto, SD (Pick: 1.2)
OF2 Corbin Carroll, ARI (Pick: 9.2)
OF3 Nick Castellanos, PHI (Pick: 13.2)
SP1 Justin Verlander, NYM (Pick: 2.9)
SP2 Julio Urias, LAD (Pick: 6.9)
SP3 Yu Darvish, SD (Pick: 8.9)
SP4 Lance Lynn, CWS (Pick: 15.2)
SP5 Freddy Peralta, MIL (Pick: 17.2)
SP6 Andrew Painter, PHI (Pick: 19.2)
RP1 Jordan Romano, TOR (Pick: 10.9)
RP2 Ryan Pressly, HOU (Pick: 11.2)
RP3 Camilo Doval, SF (Pick: 14.9)
RP4 Scott Barlow, KC (Pick: 16.9)

Team McCormick

C1 Danny Jansen, TOR (Pick: 19.3)
1B1 Luis Arraez, MIA (Pick: 8.8 | 1B/2B)
1B2 Christian Walker, ARI (Pick: 12.8)
3B1 Jose Ramirez, CLE (Pick: 1.3)
3B2 Jordan Walker, STL (Pick: 16.8)
2B1 Tommy Edman, STL (Pick: 10.8 | 2B/SS)
2B2 Gleyber Torres, NYY (Pick: 17.3)
SS1 Francisco Lindor, NYM (Pick: 3.3)
SS2 Dansby Swanson, CHC (Pick: 9.3)
OF1 Mike Trout, LAA (Pick: 4.8)
OF2 Ronald Acuna Jr., ATL (Pick: 5.3)
OF3 Oscar Gonzalez, CLE (Pick: 18.8)
SP1 Aaron Nola, PHI (Pick: 2.8)
SP2 Cristian Javier, HOU (Pick: 6.8)
SP3 Max Fried, ATL (Pick: 7.3)
SP4 Chris Bassitt, TOR (Pick: 11.3)
SP5 Clayton Kershaw, LAD (Pick: 14.8)
SP6 Jeffrey Springs, TB (Pick: 15.3 | SP/RP)
RP1 Kenley Jansen, BOS (Pick: 13.3)

Team Schoenfield

C1 Alejandro Kirk, TOR (Pick: 13.4)
1B1 Vinnie Pasquantino, KC (Pick: 9.4)
1B2 Nathaniel Lowe, TEX (Pick: 12.7)
1B3 Jose Miranda, MIN (Pick: 16.7 | 1B/3B)
2B1 Jeff McNeil, NYM (Pick: 11.4 | 2B/OF)
2B2 Andres Gimenez, CLE (Pick: 19.4)
SS1 Wander Franco, TB (Pick: 5.4)
OF1 Aaron Judge, NYY (Pick: 1.4)
OF2 Kyle Tucker, HOU (Pick: 2.7)
OF3 Julio Rodriguez, SEA (Pick: 3.4)
OF4 Lars Nootbaar, STL (Pick: 17.4)
SP1 Shane McClanahan, TB (Pick: 4.7)
SP2 Zac Gallen, ARI (Pick: 7.4)
SP3 Logan Webb, SF (Pick: 8.7)
SP4 George Kirby, SEA (Pick: 10.7)
SP5 Hunter Greene, CIN (Pick: 15.4)
RP1 Emmanuel Clase, CLE (Pick: 6.7)
RP2 Andres Munoz, SEA (Pick: 14.7)
RP3 Evan Phillips, LAD (Pick: 18.7)

Team Carty

C1 Willson Contreras, STL (Pick: 14.6)
1B1 Vladimir Guerrero Jr., TOR (Pick: 1.5)
1B2 Rhys Hoskins, PHI (Pick: 10.6)
3B1 Yandy Diaz, TB (Pick: 9.5)
3B2 Max Muncy, LAD (Pick: 13.5 | 3B/2B)
SS1 Xander Bogaerts, SD (Pick: 7.5)
OF1 Kyle Schwarber, PHI (Pick: 5.5)
OF2 Bryan Reynolds, PIT (Pick: 6.6)
OF3 Byron Buxton, MIN (Pick: 16.6)
OF4 Tyler O'Neill, STL (Pick: 19.5)
SP1 Jacob deGrom, TEX (Pick: 2.6)
SP2 Carlos Rodon, NYY (Pick: 3.5)
SP3 Luis Castillo, SEA (Pick: 4.6)
SP4 Charlie Morton, ATL (Pick: 11.5)
SP5 Luis Severino, NYY (Pick: 12.6)
SP6 Chris Sale, BOS (Pick: 15.5)
RP1 Raisel Iglesias, ATL (Pick: 8.6)
RP2 Paul Sewald, SEA (Pick: 17.5)
RP3 Clay Holmes, NYY (Pick: 18.6)

Team Mass

C1 Daulton Varsho, TOR (Pick: 9.6 | C/OF)
1B1 Freddie Freeman, LAD (Pick: 1.6)
1B2 Triston Casas, BOS (Pick: 12.5)
3B1 Rafael Devers, BOS (Pick: 3.6)
3B2 DJ LeMahieu, NYY (Pick: 17.6 | 3B/1B/2B)
2B1 Brandon Lowe, TB (Pick: 11.6)
SS1 Bo Bichette, TOR (Pick: 4.5)
OF1 Michael Harris II, ATL (Pick: 7.6)
OF2 Starling Marte, NYM (Pick: 13.6)
OF3 Andrew Vaughn, CWS (Pick: 14.5 | OF/1B)
DH1 Shea Langeliers, OAK (Pick: 18.5)
SP1 Shane Bieber, CLE (Pick: 2.5)
SP2 Tyler Glasnow, TB (Pick: 5.6)
SP3 Triston McKenzie, CLE (Pick: 6.5)
SP4 Lucas Giolito, CWS (Pick: 10.5)
SP5 Lance McCullers Jr., HOU (Pick: 15.6)
RP1 Josh Hader, SD (Pick: 8.5)
RP2 David Bednar, PIT (Pick: 16.5)
RP3 A.J. Puk, MIA (Pick: 19.6)

Team Becquey

C1 Will Smith, LAD (Pick: 5.7)
C2 MJ Melendez, KC (Pick: 13.7 | C/OF)
1B1 Pete Alonso, NYM (Pick: 3.7)
3B1 Brandon Drury, LAA (Pick: 17.7 | 3B/1B/2B)
2B1 Ketel Marte, ARI (Pick: 11.7)
2B2 Thairo Estrada, SF (Pick: 18.4 | 2B/SS)
SS1 Trea Turner, PHI (Pick: 2.4)
SS2 Bobby Witt Jr., KC (Pick: 6.4 | SS/3B)
OF1 George Springer, TOR (Pick: 7.7)
OF2 Alex Verdugo, BOS (Pick: 14.4)
OF3 Adolis Garcia, TEX (Pick: 15.7)
SP1 Gerrit Cole, NYY (Pick: 1.7)
SP2 Dylan Cease, CWS (Pick: 4.4)
SP3 Framber Valdez, HOU (Pick: 8.4)
SP4 Nestor Cortes, NYY (Pick: 9.7)
SP5 Kyle Wright, ATL (Pick: 10.4)
SP6 Luis Garcia, HOU (Pick: 12.4)
RP1 A.J. Minter, ATL (Pick: 16.4)
RP2 Daniel Bard, COL (Pick: 19.7)

Team Cockcroft

C1 Adley Rutschman, BAL (Pick: 8.3)
1B1 Anthony Rizzo, NYY (Pick: 13.8)
3B1 Alex Bregman, HOU (Pick: 4.3)
2B1 Ozzie Albies, ATL (Pick: 5.8)
2B2 Vaughn Grissom, ATL (Pick: 18.3)
SS1 Willy Adames, MIL (Pick: 11.8)
OF1 Yordan Alvarez, HOU (Pick: 2.3)
OF2 Brandon Nimmo, NYM (Pick: 9.8)
OF3 Teoscar Hernandez, SEA (Pick: 14.3)
OF4 Christian Yelich, MIL (Pick: 16.3)
SP1 Corbin Burnes, MIL (Pick: 1.8)
SP2 Spencer Strider, ATL (Pick: 3.8 | SP/RP)
SP3 Kodai Senga, NYM (Pick: 15.8)
SP4 Jesus Luzardo, MIA (Pick: 17.8)
RP1 Edwin Diaz, NYM (Pick: 6.3)
RP2 Devin Williams, MIL (Pick: 7.8)
RP3 Felix Bautista, BAL (Pick: 10.3)
RP4 Ryan Helsley, STL (Pick: 12.3)
RP5 Alexis Diaz, CIN (Pick: 19.8)

Team Soppe

C1 Sean Murphy, ATL (Pick: 15.9)
1B1 Josh Bell, CLE (Pick: 8.2)
3B1 Manny Machado, SD (Pick: 2.2)
2B1 Jorge Polanco, MIN (Pick: 14.2)
2B2 Jazz Chisholm Jr., MIA (Pick: 18.2)
SS1 Fernando Tatis Jr., SD (Pick: 4.2)
OF1 Mookie Betts, LAD (Pick: 1.9)
OF2 Luis Robert, CWS (Pick: 9.9)
OF3 Eloy Jimenez, CWS (Pick: 10.2)
OF4 Taylor Ward, LAA (Pick: 12.2)
SP1 Brandon Woodruff, MIL (Pick: 3.9)
SP2 Alek Manoah, TOR (Pick: 5.9)
SP3 Robbie Ray, SEA (Pick: 6.2)
SP4 Joe Musgrove, SD (Pick: 7.9)
SP5 Blake Snell, SD (Pick: 11.9)
SP6 Nick Lodolo, CIN (Pick: 13.9)
SP7 Jon Gray, TEX (Pick: 16.2)
SP8 Joe Ryan, MIN (Pick: 17.9)
SP9 Jack Flaherty, STL (Pick: 19.9)

Team Best

C1 J.T. Realmuto, PHI (Pick: 10.1)
C2 Salvador Perez, KC (Pick: 11.10)
1B1 Jose Abreu, HOU (Pick: 5.10)
1B2 Rowdy Tellez, MIL (Pick: 15.10)
3B1 Gunnar Henderson, BAL (Pick: 8.1)
2B1 Marcus Semien, TEX (Pick: 3.10)
SS1 Corey Seager, TEX (Pick: 4.1)
SS2 Amed Rosario, CLE (Pick: 13.10)
OF1 Steven Kwan, CLE (Pick: 6.1)
OF2 Cedric Mullins, BAL (Pick: 7.10)
OF3 Randy Arozarena, TB (Pick: 9.10)
DH1 J.D. Martinez, LAD (Pick: 17.10)
SP1 Max Scherzer, NYM (Pick: 1.10)
SP2 Sandy Alcantara, MIA (Pick: 2.1)
SP3 Brady Singer, KC (Pick: 12.1)
SP4 Jose Berrios, TOR (Pick: 14.1)
SP5 Merrill Kelly, ARI (Pick: 16.1)
SP6 Alex Cobb, SF (Pick: 18.1)
SP7 Tyler Mahle, MIN (Pick: 19.10)

Tue, 14 Feb 2023 05:37:00 -0600 en text/html https://www.espn.com/fantasy/baseball/story/_/id/35658673/fantasy-baseball-head-head-points-mock-draft-mlb-2023
Killexams : Telstra Purple to support Microsoft services with dedicated practice
Vanessa Sorenson (Microosft)

Vanessa Sorenson (Microosft)

Credit: Supplied

Telstra Purple is set to support Microsoft services for businesses with the launch of a dedicated end-to-end practice. 

Named a key part of Telstra and Microsoft’s expanded five-year agreement, which was signed in July last year, the practice combines Microsoft product, sales and delivery certified with a team of 20 and supported by connectivity and technology experts from the telco. 

Gretchen Cooke (Telstra Purple)Credit: Telstra Purple
Gretchen Cooke (Telstra Purple)

Gretchen Cooke, Telstra Purple growth and transformation executive, said the practice is anticipated to bring scale to the managed services arm’s “ability to provide maximum value for our joint customers, no matter where they are in their digital journey or the size or style of their business”.

“Our practice experts are accredited by Microsoft and have a proven track record of leading successful transformations with Microsoft solutions, from workplace migrations to designing cloud strategies with purpose,” she said.