DP-100 syllabus - Designing and Implementing a Data Science Solution on Azure Updated: 2024 | ||||||||
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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 Microsoft Implementing syllabus | ||||||||
Other Microsoft examsMOFF-EN Microsoft Operations Framework Foundation62-193 Technology Literacy for Educators AZ-400 Microsoft Azure DevOps Solutions DP-100 Designing and Implementing a Data Science Solution on Azure MD-100 Windows 10 MD-101 Managing Modern Desktops MS-100 Microsoft 365 Identity and Services MS-101 Microsoft 365 Mobility and Security MB-210 Microsoft Dynamics 365 for Sales MB-230 Microsoft Dynamics 365 for Customer Service MB-240 Microsoft Dynamics 365 for Field Service MB-310 Microsoft Dynamics 365 for Finance and Operations, Financials (2023) MB-320 Microsoft Dynamics 365 for Finance and Operations, Manufacturing MS-900 Microsoft Dynamics 365 Fundamentals MB-220 Microsoft Dynamics 365 for Marketing MB-300 Microsoft Dynamics 365 - Core Finance and Operations MB-330 Microsoft Dynamics 365 for Finance and Operations, Supply Chain Management AZ-500 Microsoft Azure Security Technologies 2023 MS-500 Microsoft 365 Security Administration AZ-204 Developing Solutions for Microsoft Azure MS-700 Managing Microsoft Teams AZ-120 Planning and Administering Microsoft Azure for SAP Workloads AZ-220 Microsoft Azure IoT Developer MB-700 Microsoft Dynamics 365: Finance and Operations Apps Solution Architect AZ-104 Microsoft Azure Administrator 2023 AZ-303 Microsoft Azure Architect Technologies AZ-304 Microsoft Azure Architect Design DA-100 Analyzing Data with Microsoft Power BI DP-300 Administering Relational Databases on Microsoft Azure DP-900 Microsoft Azure Data Fundamentals MS-203 Microsoft 365 Messaging MS-600 Building Applications and Solutions with Microsoft 365 Core Services PL-100 Microsoft Power Platform App Maker PL-200 Microsoft Power Platform Functional Consultant PL-400 Microsoft Power Platform Developer AI-900 Microsoft Azure AI Fundamentals MB-500 Microsoft Dynamics 365: Finance and Operations Apps Developer SC-400 Microsoft Information Protection Administrator MB-920 Microsoft Dynamics 365 Fundamentals Finance and Operations Apps (ERP) MB-800 Microsoft Dynamics 365 Business Central Functional Consultant PL-600 Microsoft Power Platform Solution Architect AZ-600 Configuring and Operating a Hybrid Cloud with Microsoft Azure Stack Hub SC-300 Microsoft Identity and Access Administrator SC-200 Microsoft Security Operations Analyst DP-203 Data Engineering on Microsoft Azure MB-910 Microsoft Dynamics 365 Fundamentals (CRM) AI-102 Designing and Implementing a Microsoft Azure AI Solution AZ-140 Configuring and Operating Windows Virtual Desktop on Microsoft Azure MB-340 Microsoft Dynamics 365 Commerce Functional Consultant MS-740 Troubleshooting Microsoft Teams SC-900 Microsoft Security, Compliance, and Identity Fundamentals AZ-800 Administering Windows Server Hybrid Core Infrastructure AZ-801 Configuring Windows Server Hybrid Advanced Services AZ-700 Designing and Implementing Microsoft Azure Networking Solutions AZ-305 Designing Microsoft Azure Infrastructure Solutions AZ-900 Microsoft Azure Fundamentals PL-300 Microsoft Power BI Data Analyst PL-900 Microsoft Power Platform Fundamentals MS-720 Microsoft Teams Voice Engineer DP-500 Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI PL-500 Microsoft Power Automate RPA Developer SC-100 Microsoft Cybersecurity Architect MO-201 Microsoft Excel Expert (Excel and Excel 2019) MO-100 Microsoft Word (Word and Word 2019) MS-220 Troubleshooting Microsoft Exchange Online DP-420 Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB MB-335 Microsoft Dynamics 365 Supply Chain Management Functional Consultant Expert MB-260 Microsoft Dynamics 365 Customer Insights (Data) Specialist AZ-720 Troubleshooting Microsoft Azure Connectivity 700-821 Cisco IoT Essentials for System Engineers (IOTSE) MS-721 Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate MD-102 Microsoft 365 Certified: Endpoint Administrator Associate MS-102 Microsoft 365 Administrator | ||||||||
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DP-100 Dumps DP-100 Braindumps DP-100 Real Questions DP-100 Practice Test DP-100 dumps free Microsoft 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 their 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 Excellerate 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 For More exams visit https://killexams.com/vendors-exam-list Kill your exam at First Attempt....Guaranteed! | ||||||||
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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:
The genuine financial bounty rewards will be given out for bugs related to tampering, spoofing, information disclosure, and elevation of privilege. The prices for successfully finding a Microsoft Defender bug in those areas will range from $500 to $8,000, depending on the level of severity. However, the biggest bounty amounts are for researchers who find issues in Defender related to Remote Code Execution. The rewards for that category will range from $5,000 all the way to $20,000. In October, Microsoft announced a bounty program to help find bugs related to its Bing AI services with up to $15,000 in rewards. 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 they strongly recommend moving to Windows 11, they 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 programmeAccording 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. ALSO READ: Using a smartphone for 4 hours a day may damage your mental health Inputs from IANS 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 they cross generational norms to build professional working relationships? Simultaneously, how do they 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, they lost the ability to communicate as a society. Without seeing each other face to face, they 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 they attributed academic lingo. That said, many of us lost the art of communication. Just like they must train to fight fires, they must constantly exercise their communication skills. After years of Zoom meetings, they 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 they 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. They 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 their problems is where they 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. 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. 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TD SYNNEX Corporation Price and ConsensusTD SYNNEX Corporation price-consensus-chart | TD SYNNEX Corporation Quote Growing Focus on Generative AIWe 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 UpAll 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. Want the latest recommendations from Zacks Investment Research? Today, you can obtain 7 Best Stocks for the Next 30 Days. Click to get this free report Microsoft Corporation (MSFT) : Free Stock Analysis Report TD SYNNEX Corporation (SNX) : Free Stock Analysis Report Main International ETF (INTL): ETF Research Reports Meta Platforms, Inc. (META) : Free Stock Analysis Report | ||||||||
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