[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-183633-105":59,"doc-detail-183633-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","certification-test-score-analysis-study-program-mos-word-and-mcf-ai","Certification Test Score Analysis - Study Program MOS-Word and MCF-AI","","This document presents certification study program evaluation data for two tracks: MOS-Word and MCF-AI. It lists training groups, exam group fields, training duration, and score analysis outputs, including certification test scores and questionnaire placeholders. Program-level breakdowns show topic modules and their allocation, with testing vs training data proportions and summary metrics. Detailed tables compare MOS and MCF AI participant scores to prediction scores, reporting accuracy percentages and overall performance trends across multiple 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study programs and modules are included in the certification analysis?","Question",{"text":112,"@type":113},"The analysis includes MOS-Word modules (e.g., Manage Documents, Insert and Format Text/Paragraphs/Sections, Manage Tables and Lists, Create and Manage References, Insert and Format Graphic Elements) and MCF-AI modules (AI workloads and considerations, and Azure machine learning and NLP workload principles/features).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are training and testing data proportions defined?",{"text":117,"@type":113},"The document shows a testing data portion of 30% and a training data portion of 70% for the program score analysis section.",{"name":119,"@type":110,"acceptedAnswer":120},"How is prediction performance measured for participants?",{"text":121,"@type":113},"Each participant has a MOS/MCF score, a prediction score, and an accuracy percentage, allowing comparison between actual and predicted results across participants.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},183633,1788353471,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","| Activity Name |  | Field Name |\n| --- | --- | --- |\n| Certification\u003Cbr>MOS-Word | Certification\u003Cbr>MCF-AI | Study program |\n|  |  | City of Origin |\n|  |  | Province of Origin |\n|  |  | Training Group |\n|  |  | Exam Group |\n|  |  | Trainer during Training |\n|  |  | Training Duration |\n|  |  | Score Analysis |\n|  |  | Certification Test Score |\n|  |  | Questionnaire |\n|  |  | Certification test history |\n\n\n| Program | Score Analysis |\n| --- | --- |\n| Certification\u003Cbr>MOS-Word | Manage Documents (MD) |\n|  | Insert and Format Text, Paragraphs, and Sections (IFPS) |\n|  | Manage Tables and Lists (MTL) |\n|  | Create and Manage References (CMR) |\n|  | Insert and Format Graphic Elements (IFGE) |\n| Certification\u003Cbr>MCF-AI | Describe Artificial Intelligence Workloads and Considerations (DAIWC) |\n|  | Describe Fundamental Principles of Machine Learning on Azure (DFPMLA) |\n|  | Describe Features of Computer Vision Workloads on Azure (DFCVWA) |\n|  | Describe Features of Natural Language Processing Workloads on Azure (DFNLPWA) |\n\n|  | Certification\u003Cbr>MOS-Word | Certification\u003Cbr>MCF-AI |\n| --- | --- | --- |\n| Testing data (30%) | 26 | 33 |\n| Training data (70%) | 62 | 77 |\n| Total (100%) | 88 | 110 |\n\n| N (Total population) | K (Number of criterion) | SSR | SER |\n| --- | --- | --- | --- |\n| 62 | 6 | 20162,62 | 19,26 |\n\n\n| N (Total population) | K (Number of criterion) | SSR | SER |\n| --- | --- | --- | --- |\n| 77 | 4 | 82374,72 | 33,59 |\n\n\n| No. | Participant Name | MOS Score 19 | Prediction Score | Accuracy (%) |\n| --- | --- | --- | --- | --- |\n| 1. | Participant 1 | 792 | 782 | 99% |\n| 2. | Participant 2 | 825 | 831 | 99% |\n| 3. | Participant 3 | 907 | 908 | 100% |\n| 4. | Participant 4 | 800 | 807 | 99% |\n| 5. | Participant 5 | 925 | 935 | 99% |\n| 6. | Participant 6 | 861 | 857 | 100% |\n| 7. | Participant 7 | 509 | 525 | 97% |\n| 8. | Participant 8 | 953 | 958 | 99% |\n| 9. | Participant 9 | 950 | 960 | 99% |\n| 10. | 10 participants | 775 | 779 | 99% |\n| 11. | Participant 11 | 700 | 704 | 99% |\n| 12. | 12 participants | 884 | 880 | 100% |\n| 13. | Participant 13 | 861 | 858 | 100% |\n| 14. | 14 participants | 800 | 806 | 99% |\n| 15. | 15 participants | 723 | 705 | 98% |\n| 16. | Participant 16 | 792 | 802 | 99% |\n| 17. | 17 participants | 976 | 983 | 99% |\n| 18. | 18 participants | 792 | 777 | 98% |\n| 19. | Participant 19 | 792 | 782 | 99% |\n| 20. | 20 participants | 884 | 907 | 97% |\n| 21. | Participant 21 | 700 | 678 | 97% |\n| 22. | Participant 22 | 884 | 883 | 100% |\n| 23. | Participant 23 | 838 | 832 | 99% |\n| 24. | 24 participants | 907 | 911 | 100% |\n| 25. | 25 participants | 725 | 727 | 100% |\n\n\n| No. | Participant Name | MCF Score | Prediction Score | Accuracy (%) |\n| --- | --- | --- | --- | --- |\n| 1 | Participant 1 | 731 | 721 | 99% |\n| 2 | Participant 2 | 684 | 684 | 100% |\n| 3 | Participant 3 | 700 | 694 | 99% |\n| 4 | Participant 4 | 905 | 905 | 100% |\n| 5 | Participant 5 | 669 | 657 | 98% |\n| 6 | Participant 6 | 810 | 797 | 98% |\n| 7 | Participant 7 | 669 | 662 | 99% |\n| 8 | Participant 8 | 639 | 630 | 99% |\n| 9 | Participant 9 | 700 | 707 | 99% |\n| 10 | 10 participants | 826 | 821 | 99% |\n| 11 | Participant 11 | 936 | 928 | 99% |\n| 12 | 12 participants | 778 | 777 | 100% |\n\n\n| 14 | 14 participants | 764 | 793 | 96% |\n| --- | --- | --- | --- | --- |\n| 15 | 15 participants | 517 | 516 | 100% |\n| 16 | Participant 16 | 639 | 631 | 99% |\n| 17 | 17 participants | 715 | 718 | 100% |\n| 18 | 18 participants | 747 | 730 | 98% |\n| 19 | Participant 19 | 684 | 692 | 99% |\n| 20 | 20 participants | 747 | 751 | 99% |\n| 21 | Participant 21 | 608 | 594 | 98% |\n| 22 | Participant 22 | 563 | 572 | 98% |\n| 23 | Participant 23 | 842 | 872 | 96% |\n| 24 | 24 participants | 826 | 825 | 100% |\n| 25 | 25 participants | 857 | 850 | 99% |\n| 26 | Participant 26 | 889 | 878 | 99% |\n| 27 | Participant 27 | 654 | 564 | 86% |\n| 28 | Participant 28 | 857 | 853 | 100% |\n| 29 | 29 participants | 654 | 656 | 100% |\n| 30 | 30 participants | 410 | 408 | 100% |\n| 31 | ","cbCaiiZBV7P4OSWu","https://ap.wps.com/l/cbCaiiZBV7P4OSWu","pdf",474016,"English","# Score Analysis\n## Training and Testing Data\n## Participant Prediction Results\n## Certification Criteria Breakdown","[{\"question\":\"What study programs and modules are included in the certification analysis?\",\"answer\":\"The analysis includes MOS-Word modules (e.g., Manage Documents, Insert and Format Text/Paragraphs/Sections, Manage Tables and Lists, Create and Manage References, Insert and Format Graphic Elements) and MCF-AI modules (AI workloads and considerations, and Azure machine learning and NLP workload principles/features).\"},{\"question\":\"How are training and testing data proportions defined?\",\"answer\":\"The document shows a testing data portion of 30% and a training data portion of 70% for the program score analysis section.\"},{\"question\":\"How is prediction performance measured for participants?\",\"answer\":\"Each participant has a MOS/MCF score, a prediction score, and an accuracy percentage, allowing comparison between actual and predicted results across participants.\"}]","Certification Test Score Analysis - Study Program MOS-Word and MCF-AI | PDF",25]