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Exam Code: DP-100 Designing and Implementing a Data Science Solution on Azure syllabus January 2024 by Killexams.com team

DP-100 Designing and Implementing a Data Science Solution on Azure

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
Designing and Implementing a Data Science Solution on Azure
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DP-100
Designing and Implementing a Data Science Solution
on Azure
http://killexams.com/pass4sure/exam-detail/DP-100
Question: 98
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You are analyzing a numerical dataset which contain missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature
set.
You need to analyze a full dataset to include all values.
Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Instead use the Multiple Imputation by Chained Equations (MICE) method.
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method
described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by
Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally
using the other variables in the data before filling in the missing values.
Note: Last observation carried forward (LOCF) is a method of imputing missing data in longitudinal studies. If a
person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all
subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused
by the attrition of participants in a study.
References:
https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
Question: 99
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data
submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?
A. View the explanations for the registered model in Azure ML studio.
B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
C. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
D. View the log files generated by the experiment used to train the model.
Answer: B
Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When youre ready to deploy
your model as a web service, use the following steps to enable Application Insights:
Question: 100
You are solving a classification task.
You must evaluate your model on a limited data sample by using k-fold cross validation. You start by
configuring a k parameter as the number of splits.
You need to configure the k parameter for the cross-validation.
Which value should you use?
A. k=0.5
B. k=0
C. k=5
D. k=1
Answer: C
Explanation:
Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special
case of the K-fold approach.
LOO CV is sometimes useful but typically doesnt shake up the data enough. The estimates from each fold are highly
correlated and hence their average can have high variance.
This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.
Question: 101
DRAG DROP
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute
resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the compute resources.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order.
Answer:
Explanation:
Question: 102
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data
submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?
A. View the explanations for the registered model in Azure ML studio.
B. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
C. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
D. View the log files generated by the experiment used to train the model.
Answer: B
Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When youre ready to deploy
your model as a web service, use the following steps to enable Application Insights:
Question: 103
You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a
real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an
error occurs when the service runs the entry script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-
deployment of the service for each code update.
What should you do?
A. Register a new version of the model and update the entry script to load the new version of the model from its
registered path.
B. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
C. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
D. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
E. Create a local web service deployment configuration and deploy the model to a local Docker container.
Answer: C
Explanation:
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure
Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an
Environment object as an input parameter. In this case our service will create a base docker image for you during
deployment stage and mount the required models all in one call.
The basic deployment tasks are:
Question: 104
HOTSPOT
You plan to implement a two-step pipeline by using the Azure Machine Learning SDK for Python.
The pipeline will pass temporary data from the first step to the second step.
You need to identify the class and the corresponding method that should be used in the second step to access
temporary data generated by the first step in the pipeline.
Which class and method should you identify? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point
Answer:
Question: 105
HOTSPOT
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to
remotely run the training script.
You run the following Python code:
Answer:
Explanation:
Box 1: Yes
The compute is created within your workspace region as a resource that can be shared with other users.
Box 2: Yes
It is displayed as a compute cluster.
View compute targets
Question: 106
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You train a classification model by using a logistic regression algorithm.
You must be able to explain the models predictions by calculating the importance of each feature, both as an overall
global relative importance value and as a measure of local importance for a specific set of predictions.
You need to create an explainer that you can use to retrieve the required global and local feature importance values.
Solution: Create a TabularExplainer.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Instead use Permutation Feature Importance Explainer (PFI).
Note 1:
Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to
explain classification and regression models. At a high level, the way it works is by randomly shuffling data one
feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger
the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does
not explain individual predictions.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
Question: 107
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
A. Fisher Linear Discriminant Analysis.
B. Filter Based Feature Selection
C. Synthetic Minority Oversampling Technique (SMOTE)
D. Permutation Feature Importance
Answer: C
Explanation:
Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases
in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply
duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be
imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult
to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
Question: 108
You use the following code to define the steps for a pipeline:
from azureml.core import Workspace, Experiment, Run
from azureml.pipeline.core import Pipeline
from azureml.pipeline.steps import PythonScriptStep
ws = Workspace.from_config()
. . .
step1 = PythonScriptStep(name="step1", )
step2 = PythonScriptsStep(name="step2", )
pipeline_steps = [step1, step2]
You need to add code to run the steps.
Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution. NOTE:
Each correct selection is worth one point.
A. experiment = Experiment(workspace=ws,
name=pipeline-experiment)
run = experiment.submit(config=pipeline_steps)
B. run = Run(pipeline_steps)
C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) experiment = Experiment(workspace=ws, name=pipeline-
experiment) run = experiment.submit(pipeline)
D. pipeline = Pipeline(workspace=ws, steps=pipeline_steps)
run = pipeline.submit(experiment_name=pipeline-experiment)
Answer: C,D
Explanation:
After you define your steps, you build the pipeline by using some or all of those steps.
# Build the pipeline. Example:
pipeline1 = Pipeline(workspace=ws, steps=[compare_models])
# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, Compare_Models_Exp).submit(pipeline1)
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines
Question: 109
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains
a unique solution that might meet the stated goals. Some question sets might have more than one correct solution,
while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not
appear in the review screen.
You create an Azure Machine Learning service datastore in a workspace.
The datastore contains the following files:
/data/2018/Q1.csv
/data/2018/Q2.csv
/data/2018/Q3.csv
/data/2018/Q4.csv
/data/2019/Q1.csv
All files store data in the following format:
id,f1,f2i
1,1.2,0
2,1,1,
1 3,2.1,0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the
following code:
Solution: Run the following code:
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Use two file paths.
Use Dataset.Tabular_from_delimeted, instead of Dataset.File.from_files as the data isnt cleansed.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets
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Data loss prevention isn't rocket science, but NASA hasn't made it work in Microsoft 365 No result found, try new keyword!Privacy review finds breach response plan is a mess, training could be better, but protection regime mostly holds up NASA's Office of Inspector General has run its eye over the aerospace agency's ... Wed, 20 Dec 2023 14:31:13 -0600 en-us text/html https://www.msn.com/ Microsoft launches its new Defender Bounty Program with up to $20,000 in rewards0 0
The Microsoft Defender logo with the Microsoft HQ logo on the background

Microsoft has launched yet another of its bounty programs that encourages security researchers to find bugs and issues in its software products with the possibility of getting awarded some big money. This time the bounty program is, ironically, designed to help find issues in the Microsoft Defender lineup of security products.

In a blog post, Microsoft stated:

The Microsoft Defender brand encompasses a variety of products and services designed to enhance the security of the Microsoft customer experience. The Microsoft Defender Bounty Program invites researchers across the globe to identify vulnerabilities in Defender products and services and share them with our team. The Defender program will begin with a limited scope, focusing on Microsoft Defender for Endpoint APIs, and will expand to include other products in the Defender brand over time.

The company revealed more details on the bounty program on its own dedicated page. Among other things it goes over the criteria that security researchers must go over to be eligible to win a bug bounty prize:

  • Identify a vulnerability in listed in-scope Defender products that was not previously reported to, or otherwise known by, Microsoft.
  • Such vulnerability must be Critical or Important severity and reproducible on the latest, fully patched version of the product or service.
  • Include clear, concise, and reproducible steps, either in writing or in video format.
  • Provide our engineers the information necessary to quickly reproduce, understand, and fix the issue.  

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Microsoft now offers support of extended security update (ESU) program till 2025

Microsoft has recently announced that it will be launching an extended security update (ESU) program for Windows 10. The change will be implemented after the support for the operating system (OS) which is ending in October 2025.

Similar to the Windows 7 ESU programme, Microsoft will be continuing to support the operating system for three more years beyond the cut-off date o.e., 2025 for the consumers who were interested in paying for it.

In the blog post, Jason Leznek, a member of Microsoft's Windows Servicing & Delivery team said,  "While we strongly recommend moving to Windows 11, we understand there are circumstances that could prevent you from replacing Windows 10 devices before the EOS (end of support) date.”

he further added, "Therefore, Microsoft will offer Extended Security Updates.”

Windows 10 ESU programme

According to Leznek, the Windows 10 ESU program will only provide 0mportant and critical security updates. Patches for feature requests, minor defects or other changes will not be considered and technical help will be restricted to the security issues.

Furthermore, Microsoft will enable Windows 10 users to try the Copilot- an AI-powered feature, which was only available in Windows 11, earlier.

How to use the Copilot feature on Windows 10?

To use the feature, users with eligible devices (running on Windows 10) will have to install a Release Preview build which will include access to the Copilot feature.

Users will need to enrol in the Windows Insider tester program to install the preview build and potentially try out Copilot on Windows 10 Home or Pro.

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Inputs from IANS

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Implementing a Stay Interview Program in Your Fire Department

The past three years saw an upheaval in traditional workplace attitudes and practices. The fire service is no different. From the Great Resignation to quiet quitting, no department is immune to staffing crunches, mental health issues and turnover.

Fire departments, and government overall, have seen a dwindling number of applicants. Those who are hired often leave, which can cost departments upward of $70,000 to fill each vacancy. Furthermore, morale continues to sink, as generational divides and expectations drive wedges between new employees and management.

Perhaps your department invested in employee assistance programs, pay raises, wellness incentives and/or expanded leave opportunities. Why, then, would employees continue to leave or remain dissatisfied?

Ask yourself how much communication happens up and down the hierarchy. Too often, I observed officers who utterly failed to build relationships with their firefighters. This includes company officers to the fire chief. Therein lies one of the most intractable and complicated issues with the retention discussion. How do we cross generational norms to build professional working relationships? Simultaneously, how do we keep the tenured employees in times of change?

One solution that’s gaining popularity is the stay interview. Whereas traditional exit interviews probe employees’ thoughts upon their departure, a stay interview engages a current employee. Informal and periodic, these discussions allow management to extract what Stacey Cunningham of Aegis Performance Solutions calls buried treasure out of their ranks. Richard Finnegan, who is a human resources scholar, has observed a 20 percent reduction in turnover, all without spending a penny.

Younger generations

Millennials and Gen Zs have been labeled the “why” generations. They unceasingly, often to the point of madness (I can say this, because I am one), ask why every order is to be carried out. Conflict is inevitable, as continual questioning is at odds with traditional bureaucratic authority.

However, this thinking has tremendous potential when utilized constructively. Millennial employees have deep insight into what’s effective and what’s superfluous. They also want to share those insights. When was the last time that you sat down with a 20-something and asked for that individual’s input? If they don’t understand the reasoning behind decisions or believe that they have any say, good luck getting them to champion your initiatives.

Millennials desperately want to support a cause. They intertwine their public and private lives.

Mission and vision statements often are seen as mere jargon.

Many senior members express hesitation around interactions with younger people because of a perceived or, often, legitimate fear of offending them. Silence is the result. There are no relationships, no sense of community.

Senior members

On the flip side, high-performing senior employees offer a wealth of organizational and technical knowledge that’s waiting to be tapped. Many of them, frustrated with the current state of affairs, are counting down the days.

Losing productive senior members is the death knell for the fire service. You simply can’t replace a firefighter who has 20-plus years on the job. Of course, there are exceptions, but senior firefighters are your best instructors and protectors. They know when a roof isn’t safe, when conditions are deteriorating and when someone isn’t coping well with a difficult run. The value that they provide to the organization is priceless.

Adversarial relationships add no value. Both groups have value to offer, have different solutions to the same problem and/or find different problems that are worth fixing. They both want to be heard. You must work with both of them. Optimally, you must harmonize the two.

Communication skills

COVID brought about remote work and schooling. In the blink of an eye, we lost the ability to communicate as a society.

Without seeing each other face to face, we can’t decipher body language, inflection or other nonverbal cues that encompass communication. Stay interviews provide a formal process to relearn the diminishing art of face-to-face communication.

Think of your most memorable boss or leader. My elementary school principal still stands out. In a school of more than 700 students, he knew every student, teacher and parent by their first name. Years later, I saw him at a high school football game, and he still remembered both my father’s name and mine. I have no doubt that he’d remember me today. The fact that my elementary school was the best performing school in the county was of no surprise to anyone. That principal was doing stay interviews long before we attributed academic lingo.

That said, many of us lost the art of communication. Just like we must train to fight fires, we must constantly exercise our communication skills. After years of Zoom meetings, we can’t expect to jump into easy and flowing conversations (although some extroverts might disagree). Stay interviews provide a framework to build relationships, increase communication and extract valuable information.

The process

Stay interviews are conversations between supervisors and/or upper management with employees. “Skip levels” are a variation, where an interview is held between an employee and their boss’ boss. Alternatively, in smaller organizations, the chief executive is the one who conducts stay interviews.

Interviews should be conducted annually at the most, although there’s potential in scheduling them according to a strategic planning process. This entails that every employee is interviewed once in the 3–5-year planning cycle.

Information that’s gleaned during this process is particularly useful when crafting strategic outlooks. Be sure to communicate to your interviewers what information is sought and repositories for storage.

Stay interviews are relatively informal. They should never be tied to annual performance evaluations. Try to get out of the office to meet employees in a common area, park or local coffee shop.

Ask such questions as, “Why do you stay?” “Why did you leave previous jobs?” “What can we do better or differently to support your role?”

Determining questions ahead of time gives you ideas for where to steer the conversation during awkward pauses.

Most importantly, be sure to restate employees’ answers back to them in your own words. You want to be sure that you understand their attitudes and opinions. With active listening, you show your employees that their contributions are vital while you solidify the information in your own head. This disciplined and focused approach lays the foundation for enduring relationships.

Before it’s too late

If the thought of interviewing every employee is too daunting, reach out to high-performers initially.

Ask department heads and division or battalion chiefs to submit names of individuals who they believe are high-performers.

Generally, you can figure out who these individuals are rather easily. Price’s Law holds that roughly 50 percent of the work is done by the square root of the total number of employees. This is confirmed over various industries. Building relationships with the individuals who are the backbone of an organization is imperative to continued success.

One organization that I worked with wanted to capture and build relationships with new employees. We were able to design a program by which supervisors held stay interviews at 30, 60 and 90 days into members’ employment. The organization combined this with a program to interview high-performers to boost their communication. All of this was recommended without requesting a single purchase. There is no more cost-effective tool to reduce turnover than communication that’s generated via a stay interview.

Often, the answer to our problems is where we least want to look. Exit interviews capture information too late, but stay interviews extract that information before a resignation.

Wading into the ranks might not be your idea of fun, but it’s necessary if you want to earn the trust of your subordinates. Whether you are a company officer or the fire chief, you must build relationships with your people. You must talk with them. Stay interviews offer the blueprint to restore organizational communication. The longer that you put them off, the more necessary they will become.

Mon, 11 Dec 2023 01:36:00 -0600 text/html https://www.firehouse.com/careers-education/article/53080273/implementing-a-stay-interview-program-in-your-fire-department
Gretel Debuts on Microsoft Azure Marketplace & Selected for Microsoft for Startups Pegasus Program No result found, try new keyword!Additionally, Gretel is joining Microsoft for Startups Pegasus Program to facilitate the adoption of responsible AI practices across industries. Enterprises require abundant sources of secure ... Tue, 05 Dec 2023 20:45:00 -0600 https://www.businesswire.com/news/home/20231206786976/en/Gretel-Debuts-on-Microsoft-Azure-Marketplace-Selected-for-Microsoft-for-Startups-Pegasus-Program TD SYNNEX (SNX) Unveils a Partner Program for Microsoft Copilot

TD SYNNEX SNX recently unveiled the Enablement Journey program for Microsoft's MSFT 365 Copilot generative artificial intelligence (AI) offering. This unique program is designed to equip its distribution partners with the technical enablement to leverage the Microsoft 365 Copilot AI-powered workplace productivity tool.

The newly introduced Enablement Journey program offers resources and training, including Copilot Practice Builder and "Get Ready for Copilot Workshop," allowing partners to unleash AI products and services that run on Microsoft’s 365 Copilot to gain a competitive edge in their workplace and enhance productivity.

Additionally, this program will provide its partners with early access to Copilot’s offerings, including sales and technical enablement, training and required tools to help solution providers bring generative AI technology to small and medium businesses (SMBs). It will also provide insights to help companies capitalize on Copilot’s enterprise-grade security, privacy, compliance and responsible AI solutions.

Therefore, TD SYNNEX is expected to gain solid traction across SMBs on the back of the Enablement Journey program and its long-standing relationship with Microsoft.

TD SYNNEX Corporation Price and Consensus

TD SYNNEX Corporation Price and Consensus

TD SYNNEX Corporation price-consensus-chart | TD SYNNEX Corporation Quote

Growing Focus on Generative AI

We note that the latest move is in sync with the company’s efforts towards boosting its generative AI efforts.

Apart from the unveiling of the Enablement Journey program, TD SYNNEX revealed that Microsoft 365 Copilot is now a part of its Destination AI program, which it launched in August 2023. According to the company, Destination AI is a comprehensive resource aggregation of TD SYNNEX’s several AI services that are available for resellers to capture AI, machine learning and advanced analytics opportunities in the rapidly evolving AI marketplace.

The company’s sustained focus on enhancing its capabilities in distributing AI-enabled products and services has been helping it win new distribution deals from several tech companies.

In October 2023, TD SYNNEX was chosen by Meta Platforms META to be its exclusive North American distributor for the company’s new suite of business products, including the Meta Quest 3 headset and related software.

Meta's distribution agreement extends to its other generative AI products, including recently launched stickers, editing tools and AI-powered smart glasses.

Additionally, TD SYNNEX announced its partnership with Intel INTL in late November to distribute Intel Geti, an AI-based platform for image and video analysis, following its successful Destination AI program launch in the United States and Europe.

In October, the company collaborated with Intel through its wholly-owned subsidiary, Hyve Solutions Corporation, to support Intel's 5th Gen Xeon Scalable Processor, which is set to launch in Q4 2023, enhancing scalability and flexibility for AI and cloud-based operations.

Wrapping Up

All the above-mentioned endeavors will likely strengthen TD SYNNEX’s presence in the booming generative AI space.

Per a Fortune Business Insights report, the global generative AI market size is expected to reach $667.96 billion by 2030, exhibiting a CAGR of 47.5% between 2023 and 2030.

Strength in the promising generative AI market will likely aid this Zacks Rank #4 (Sell) company in instilling investors’ optimism in the stock.

Moreover, the company’s continuous partnerships with other tech companies will expand its global presence and strengthen its product portfolio, which, in turn, will bolster its overall financial performance in the upcoming period.

TD SYNNEX expects to generate revenues between $14 billion and $15 billion for the fourth quarter. The Zacks Consensus Estimate for fourth-quarter revenues is pegged at $14.6 billion. Currently, shares of SNX have returned 6.7% on a year-to-date basis.

Intel sports a Zacks Rank #1 (Strong Buy) at present, while Microsoft and Meta carry a Zacks Rank #3 (Hold) each. You can see the complete list of today’s Zacks #1 Rank stocks here.

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Mon, 11 Dec 2023 19:26:00 -0600 en-US text/html https://finance.yahoo.com/news/td-synnex-snx-unveils-partner-142600377.html
Sony hit with hefty fine over PS4 controller row No result found, try new keyword!Sony has been criticized for years for limiting the ability of third-party manufacturers to make economically priced accessory alternatives. Tue, 02 Jan 2024 22:26:00 -0600 en-us text/html https://www.msn.com/




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