Itcertkr는 가장 효율높은 Salesforce Analytics-Con-202시험대비방법을 가르쳐드립니다. 저희 Salesforce Analytics-Con-202덤프는 실제 시험문제의 모든 범위를 커버하고 있어 Salesforce Analytics-Con-202덤프의 문제만 이해하고 기억하신다면 제일 빠른 시일내에 시험패스할수 있습니다. 경쟁율이 심한 IT시대에 Salesforce Analytics-Con-202시험 패스만으로 이 사회에서 자신만의 위치를 보장할수 있고 더욱이는 한층 업된 삶을 누릴수도 있습니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Workspaces & Orgs | 10% | - Asset Deployment
|
| Topic 2: Embedding, Cross-Cloud, & Interoperability | 20% | - Salesforce Analytics Integration
|
| Topic 3: Data Setup | 20% | - Semantic Models
|
| Topic 4: Basic Setup & Admin | 10% | - Access Management
|
| Topic 5: Agentic Experiences | 25% | - Generative AI
|
| Topic 6: Visualizations & Dashboards | 15% | - Visualization and Dashboard Design
|
>> Analytics-Con-202적중율 높은 시험대비덤프 <<
우리Itcertkr 사이트에Salesforce Analytics-Con-202관련자료의 일부 문제와 답 등 문제들을 제공함으로 여러분은 무료로 다운받아 체험해보실 수 있습니다. 여러분은 이것이야 말로 알맞춤이고, 전면적인 여러분이 지금까지 갖고 싶었던 문제집이라는 것을 느끼게 됩니다.
질문 # 49
A Tableau Next Consultant is configuring a Salesforce action in a Tableau Next table visualization that displays Contact records. The goal is to give sales reps the ability to trigger a screen flow that adds selected Contacts to a Campaign. What is a required configuration step for the action to work correctly?
정답：C
설명：
A Salesforce action executed from a Tableau Next visualization must know which Salesforce record the user is acting upon. Salesforce documentation therefore requires the consultant to select the appropriate record ID when configuring the action. The record ID is the primary identifier for the Salesforce object, and it must be available as part of the visualization. For Contact-based actions, this means mapping the Contact ID so that Tableau Next can invoke the action against the correct Contact record.
The multi-row selection setting in A is not the fundamental record-context requirement for a standard Salesforce action. Likewise, B is misleading. Users certainly require authorization to execute the underlying Salesforce action, but Tableau Next does not require the consultant to create a special Tableau-specific custom permission set merely to map the action.
Architecturally, Tableau Next integrates the analytical and transactional layers by associating visualization data with Salesforce object identifiers. Once a user selects the relevant data element, Tableau Next can pass the corresponding record identity into the configured Salesforce action.
References/Topics: Visualizations and Dashboards - > Add Actions to Visualizations - > Salesforce Actions -
> Record ID Mapping.
질문 # 50
A Tableau Next Consultant is asked to configure streaming data ingestion. Where is the output written?
정답：B
설명：
A Data Stream Object (DSO) is the output associated with streaming data ingestion in this question.
Streaming ingestion is designed to continuously accept incoming records from a connected source and represent that stream in Data 360 so downstream processing and analytics can work with newly arriving data with minimal delay.
A Data Lake Object is used as a persisted data-layer object for broader ingestion and storage scenarios, while a Semantic Model is a downstream analytical abstraction that defines business-friendly measures, dimensions, relationships, metrics, and other semantic logic. Neither option describes the streaming-ingestion output requested by this item.
The exam distinction is to keep the ingestion pipeline separate from the semantic layer. Streaming data first enters through the streaming/data-stream construct, where the Data Stream Object represents the incoming stream. That data can then participate in downstream mapping, modeling, and Tableau Next analytics. The semantic model does not serve as the raw ingestion target; it consumes prepared and governed data structures to provide analytical meaning.
References/Topics: Data Setup - > Streaming Data Ingestion - > Data Stream Objects.
질문 # 51
A Tableau Next Consultant needs to migrate dashboards from a sandbox org to production. Which deployment mechanism should be used?
정답：C
설명：
Data Kits are a native supported deployment mechanism for moving Tableau Next assets from a sandbox to a production Data 360 home org. Salesforce explicitly states that Tableau Next workspaces, visualizations, and dashboards can be deployed through Data Kits, and that Data 360 semantic models on which those analytical assets depend must also use Data Kits for deployment to target home orgs.
CSV export/import is a data-transfer technique, not a Tableau Next metadata lifecycle-management mechanism. It cannot preserve dashboard structure, visualization definitions, semantic-model dependencies, workspace metadata, or the relationships between these assets. Manual recreation would be operationally inefficient, prone to configuration drift, and unnecessary when supported deployment tooling exists.
A technically complete implementation must also account for dependency sequencing. When a Data Kit contains the semantic model and downstream Tableau Next content, Salesforce requires the publishing order to be at least Semantic Models - > Workspaces - > Visualizations - > Dashboards.
References/Topics: Managing Workspaces and Orgs - > Sandbox Development - > Data Kits - > Production Deployment - > Asset Dependencies.
질문 # 52
A Tableau Next Consultant has enabled Acceleration for a connected data source, which is configured to refresh data as frequently as every 15 minutes. However, records that have been deleted from the source system are still appearing in Tableau Next 30 minutes later. What is the most likely reason for this behavior?
정답：B
설명：
When Acceleration for Data Connections is enabled, Tableau Next temporarily caches external-source data in Data 360 to reduce runtime latency and improve query performance. Two refresh approaches are available:
Full Refresh and Incremental Refresh. Salesforce explicitly states that an incremental refresh processes records that have been added or changed since the previous refresh but does not update deleted data.
Therefore, records removed from the source can remain present in the accelerated cache even after several incremental refresh cycles.
This explains why the 15-minute frequency does not solve the issue. Frequency controls how often incremental processing occurs; it does not change what the incremental algorithm processes. Option A incorrectly attributes the behavior to ordinary cache latency. Option C is also too broad because acceleration itself supports deletion synchronization when a Full Refresh is used. A full refresh removes the existing cached dataset and replaces it with the current source dataset.
References/Topics: Data Setup - > Data Connections - > Acceleration - > Full versus Incremental Refresh -
> Cache Refresh Method.
질문 # 53
A Tableau Next Consultant is shadowing a client's business analyst who is building a new profitability dashboard in Tableau Next. The analyst knows exactly the business logic they need for a complex "Year- over- Year Weighted Margin" metric, but they are new to the platform and keep getting syntax errors when trying to write the formula. How does generative AI in Tableau Next assist the analyst in this scenario?
정답：B
설명：
Tableau Next provides generative AI capabilities that translate a user's natural-language description of a calculation into a calculated-field formula. This directly addresses scenarios where an analyst understands the required business logic but lacks familiarity with Tableau Next formula syntax.
With Draft with Einstein, the user describes the calculation they need in natural language. Einstein can propose the field name, description, data type, aggregation type, and formula. The agentic calculated-field experience also supports conversational refinement, allowing the user to explain requirements and respond to clarification questions before reviewing the proposed field.
A batch transform, as described in A, is a data-preparation mechanism and is unrelated to helping an analyst overcome formula-authoring syntax problems. C is less accurate because the defining generative-AI capability being tested is the transformation of natural-language analytical intent into a proposed formula.
The generated output remains subject to user review before creation, preserving human control over the final semantic definition while substantially reducing formula-authoring friction.
References/Topics: Agentic Experiences - > Generative AI - > Draft with Einstein/Data Pro - > Natural- Language Calculated Fields.
질문 # 54
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Itcertkr에서는 가장 최신이자 최고인Salesforce인증 Analytics-Con-202시험덤프를 제공해드려 여러분이 IT업계에서 더 순조롭게 나아가도록 최선을 다해드립니다. Salesforce인증 Analytics-Con-202덤프는 최근 실제시험문제를 연구하여 제작한 제일 철저한 시험전 공부자료입니다. Salesforce인증 Analytics-Con-202시험준비자료는 Itcertkr에서 마련하시면 기적같은 효과를 안겨드립니다.
Analytics-Con-202인기자격증 인증시험덤프: https://www.itcertkr.com/Analytics-Con-202_exam.html