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Salesforce-AI-Associate日本語受験攻略、Salesforce-AI-Associate練習問題

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Salesforce Salesforce-AI-Associate 認定試験の出題範囲：

トピック
出題範囲

トピック 1

AI の基礎: このトピックでは、Salesforce における AI の主要な原則とアプリケーションについて説明します。また、さまざまな種類の AI とその機能にも焦点を当てています。

トピック 2

AI のためのデータ: データ品質の重要性およびデータ品質のさまざまな要素やコンポーネントに関する質問は、このトピックに関連しています。

トピック 3

AI の倫理的考慮事項: 機械学習における人間の偏見、透明性の欠如など、AI の倫理的課題を詳しく掘り下げます。このトピックでは、Salesforce の信頼できる AI 原則を特定のシナリオに適用する方法についても説明します。

トピック 4

CRM における AI 機能: AI の利点と CRM の機能について理解します。

>> Salesforce-AI-Associate日本語受験攻略 <<

Salesforce-AI-Associate日本語受験攻略 & 認証の成功を保証, 簡単なトレーニング方法 & Salesforce Salesforce Certified AI Associate Exam

ますます多くの企業が、候補者のSalesforce-AI-Associate認定に高い注意を払うようになっていることがわかっています。これらの企業のリーダーはこれらの候補者を深く理解するのが難しいため、候補者が獲得したSalesforce-AI-Associate認定により、すべてのリーダーが自社の優秀な労働者を選択する最良かつ迅速な方法です。 Salesforce-AI-Associate認定が多くの人々にとってますます重要になっていることは間違いありません。また、Salesforce-AI-Associate試験問題もあります。 Salesforce-AI-Associate認定を簡単に取得できます。

Salesforce Certified AI Associate Exam 認定 Salesforce-AI-Associate 試験問題 (Q52-Q57):

質問 # 52
What should be done to prevent bias from entering an AI system when training it?

A. Use alternative assumptions.
B. Include Proxy variables.
C. Import diverse training data.

正解：C

解説：
"Using diverse training data is what should be done to prevent bias from entering an AI system when training it. Diverse training data means that the data covers a wide range of features and patterns that are relevant for the AI task. Diversetraining data can help prevent bias by ensuring that the AI system learns from a balanced and representative sample of the target population or domain. Diverse training data can also help improve the accuracy and generalization of the AI system by capturing more variations and scenarios in the data."

質問 # 53
Which best describes the different between predictive AI and generative AI?

A. Predictive AI uses machine learning to classes or predict output from its input data whereas generative AI does not use machine learning to generate its output
B. Predictive AI and generative have the same capabilities differ in the type of input they receive:
predictive AI receives raw data whereas generation AI receives natural language.
C. Predictive new and original output for a given input.

正解：C

解説：
Explanation
"The difference between predictive AI and generative AI is that predictive AI analyzes existing data to make predictions or recommendations based on patterns or trends, while generative AI creates new content based on existing data or inputs. Predictive AI is a type of AI that uses machine learning techniques to learn from existing data and make predictions or recommendations based on the data. For example, predictive AI can be used to forecast sales, revenue, or demand based on historical data and trends. Generative AI is a type of AI that uses machine learning techniques togenerate novel content such as images, text, music, or video based on existing data or inputs. For example, generative AI can be used to create realistic faces, write summaries, compose songs, or produce videos."

質問 # 54
Which best describes the difference between predictive AI and generative Al?

A. Predictive AT uses machine learning to classify or predict outputs from its input data whereas generative Al does not use machine learning to generate its output.
B. Predictive Al uses machine learning to classify or predict outputs from its input data whereas generative Al uses machine learning to generate new and original output for 4 given input
C. Predictive Al and generative Al have the same capabilities but differ in the type of input they receive; predictive AT receives raw data whereas generative AT receives natural language.

正解：B

解説：
Predictive AI and generative AI represent two different applications of machine learning technologies.
Predictive AI focuses on making predictions based on historical data. It analyzes past data to forecast future outcomes, such as customer churn or sales trends. On the other hand, generative AI is designed to generate new and original outputs based on the learned data patterns. This includes tasks like creating new images, text, or music that resemble the training data but do not duplicate it. Both types of AI use machine learning, but their objectives and outputs are distinct. For detailed differences and applications in a Salesforce context, Salesforce's guide on AI technologies is a helpful resource, accessible at Salesforce AI Technologies.

質問 # 55
A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?

A. Completeness
B. Usage
C. Duplication

正解：A

解説：
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness.
Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights.
Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.

質問 # 56
What are the key components of the data quality standard?

A. Reviewing, Updating, Archiving
B. Accuracy, Completeness, Consistency
C. Naming, formatting, Monitoring

正解：B

解説：
Explanation
"Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources."

質問 # 57
......

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