The architecture of Oracle Fusion Data Intelligence is designed to facilitate the integration, processing, and analysis of data across various sources and formats. It employs a layered approach that includes data ingestion, processing, storage, and visualization components. Understanding this architecture is crucial for implementing effective data solutions. The architecture typically consists of several key components: data connectors for integrating disparate data sources, a processing engine for transforming and analyzing data, a storage layer for managing data efficiently, and visualization tools for presenting insights. Each layer interacts with the others to ensure seamless data flow and accessibility. For instance, the data connectors must be compatible with the processing engine to ensure that data can be transformed correctly. Additionally, the choice of storage solutions can impact the performance of data retrieval and analysis. Therefore, a comprehensive understanding of how these components work together is essential for optimizing data workflows and ensuring that the architecture meets the specific needs of an organization.

In the realm of Oracle Fusion Data Intelligence, security and compliance are paramount, especially when dealing with sensitive data across various industries. Organizations must ensure that their data handling practices align with regulatory requirements and internal policies. One critical aspect of this is understanding the role of data classification in establishing security protocols. Data classification involves categorizing data based on its sensitivity and the impact that unauthorized access could have on the organization. This process helps in determining the appropriate security measures needed to protect the data. For instance, data classified as highly sensitive may require encryption, strict access controls, and regular audits, while less sensitive data might have more lenient security measures. The scenario presented in the question emphasizes the importance of implementing a robust data classification framework to ensure compliance with regulations such as GDPR or HIPAA. By doing so, organizations can mitigate risks associated with data breaches and ensure that they are prepared to respond effectively to any incidents. Understanding the nuances of data classification and its implications for security and compliance is essential for professionals in the field.

Oracle Fusion Data Intelligence is a comprehensive platform designed to facilitate data management, analytics, and integration across various business processes. Understanding its architecture and components is crucial for effective implementation. The platform emphasizes the importance of data governance, quality, and security, which are essential for organizations aiming to leverage data for strategic decision-making. In a scenario where a company is looking to implement Oracle Fusion Data Intelligence, it is vital to recognize how the platform’s features can be aligned with the organization’s data strategy. This includes understanding the role of data lakes, data warehouses, and the integration of machine learning capabilities to enhance data insights. Additionally, the platform supports various data sources and formats, making it adaptable to different business environments. The ability to create a unified view of data across disparate systems is a key advantage, enabling organizations to derive actionable insights and improve operational efficiency. Therefore, a nuanced understanding of how these components interact and contribute to the overall data ecosystem is essential for successful implementation.

In the realm of data management, conceptual data models serve as a foundational framework that outlines the structure of data within an organization. They provide a high-level view of data entities, their attributes, and the relationships between them, without delving into the specifics of how the data will be stored or processed. This abstraction is crucial for aligning business requirements with technical implementations. When developing a conceptual data model, it is essential to engage stakeholders to ensure that the model accurately reflects the business domain and its needs. The model should be flexible enough to accommodate changes in business processes or data requirements over time. Additionally, it is important to differentiate between conceptual, logical, and physical data models, as each serves a distinct purpose in the data architecture. A conceptual model focuses on the “what” of the data, while logical models address the “how” in terms of data relationships and structures, and physical models deal with the actual implementation details. Understanding these distinctions is vital for professionals involved in data intelligence projects, as it influences how data is captured, stored, and utilized across various applications.

In decision analysis, particularly within the context of Oracle Fusion Data Intelligence, it is crucial to understand how to evaluate different alternatives based on their potential outcomes and associated risks. Decision analysis often involves the use of various tools and methodologies to assess the implications of different choices. One common approach is to utilize decision trees, which visually represent the possible outcomes of decisions, allowing for a clearer understanding of the potential benefits and drawbacks of each option. Additionally, factors such as uncertainty, stakeholder impact, and resource allocation must be considered when making decisions. In the scenario presented, the decision-maker must weigh the potential benefits of a new data integration tool against the risks of implementation challenges and the potential disruption to existing workflows. This requires a nuanced understanding of both the technical aspects of the tool and the broader organizational context. The correct answer reflects a comprehensive approach to decision-making that incorporates both qualitative and quantitative analyses, ensuring that the chosen option aligns with the organization’s strategic goals while minimizing risks.

User training and support are critical components in the successful implementation of Oracle Fusion Data Intelligence. Effective training ensures that users are not only familiar with the system’s functionalities but also understand how to leverage these capabilities to enhance their workflows and decision-making processes. A well-structured training program should address various learning styles and provide ongoing support to accommodate users’ evolving needs. This includes creating comprehensive training materials, conducting hands-on workshops, and offering access to a support team for troubleshooting and guidance. Additionally, it is essential to assess the effectiveness of the training through feedback mechanisms and performance metrics, allowing for continuous improvement of the training program. By fostering a culture of learning and support, organizations can maximize user adoption and minimize resistance to new technologies, ultimately leading to a more successful implementation of Oracle Fusion Data Intelligence.

In the realm of advanced analytics, Oracle Fusion Data Intelligence offers a suite of features that enable organizations to derive actionable insights from their data. One of the key aspects of these features is the ability to implement predictive analytics, which involves using historical data to forecast future outcomes. This is particularly useful in various industries, such as finance, healthcare, and retail, where understanding future trends can significantly impact decision-making processes. For instance, in a retail scenario, predictive analytics can help determine which products are likely to be in demand during a specific season based on past sales data, customer behavior, and market trends. This allows businesses to optimize inventory levels, enhance customer satisfaction, and ultimately increase profitability. Moreover, advanced analytics features also encompass machine learning algorithms that can identify patterns and anomalies in large datasets, providing deeper insights that traditional analytics might overlook. Understanding how to leverage these features effectively is crucial for professionals in the field, as it enables them to not only analyze data but also to make informed predictions that drive strategic initiatives.

In the context of Oracle Fusion Applications, integration is a critical aspect that enables seamless data flow and operational efficiency across various business functions. When integrating with Oracle Fusion Applications, it is essential to understand the different integration patterns and their implications on data consistency, performance, and user experience. One common integration approach is the use of Oracle Integration Cloud, which facilitates the connection between Oracle Fusion Applications and other systems, whether they are on-premises or cloud-based. This integration can be achieved through various methods, including REST APIs, SOAP web services, and file-based data import/export. A key consideration in this integration process is the handling of data synchronization and transformation. For instance, when integrating customer data from an external CRM system into Oracle Fusion Applications, it is crucial to ensure that the data adheres to the required formats and business rules defined within the Oracle ecosystem. Additionally, understanding the role of middleware in managing these integrations can significantly impact the overall architecture and performance of the solution. Moreover, organizations must also consider security aspects, such as authentication and authorization, to protect sensitive data during the integration process. This comprehensive understanding of integration strategies and their implications is vital for successfully implementing Oracle Fusion Data Intelligence solutions.

Emerging technologies in data analytics are reshaping how organizations derive insights from their data. One of the most significant advancements is the integration of artificial intelligence (AI) and machine learning (ML) into data analytics processes. These technologies enable predictive analytics, which allows businesses to forecast trends and behaviors based on historical data. For instance, AI algorithms can analyze vast datasets to identify patterns that may not be immediately apparent to human analysts. This capability enhances decision-making processes by providing data-driven insights that can lead to more effective strategies. Another important aspect is the use of cloud computing, which facilitates the storage and processing of large volumes of data. Cloud platforms offer scalability and flexibility, allowing organizations to adjust their resources based on demand. This is particularly beneficial for businesses that experience fluctuating data needs. Furthermore, the rise of real-time analytics has transformed how companies respond to market changes, enabling them to act swiftly based on current data rather than relying solely on historical trends. In addition, the incorporation of natural language processing (NLP) allows users to interact with data analytics tools using everyday language, making these technologies more accessible to non-technical users. This democratization of data analytics empowers a broader range of stakeholders to engage with data, fostering a culture of data-driven decision-making across the organization.

Data modeling is a critical aspect of data management that involves creating a conceptual representation of data structures and their relationships. In the context of Oracle Fusion Data Intelligence, effective data modeling ensures that data is organized in a way that supports business processes and analytics. A well-structured data model can facilitate data integration, improve data quality, and enhance decision-making capabilities. When designing a data model, it is essential to consider various factors such as normalization, denormalization, and the specific requirements of the business domain. Normalization helps eliminate redundancy and ensures data integrity, while denormalization may be employed for performance optimization in analytical queries. Additionally, understanding the relationships between different data entities—such as one-to-one, one-to-many, and many-to-many—is crucial for accurately representing the data landscape. The choice of data modeling techniques, such as entity-relationship diagrams or dimensional modeling, can significantly impact the effectiveness of data utilization within an organization. Therefore, a nuanced understanding of these principles is vital for professionals working with Oracle Fusion Data Intelligence.

Data governance is a critical aspect of managing data within an organization, ensuring that data is accurate, available, and secure. It encompasses the policies, procedures, and standards that dictate how data is managed and utilized. In the context of Oracle Fusion Data Intelligence, effective data governance involves establishing clear roles and responsibilities, implementing data quality measures, and ensuring compliance with relevant regulations. A well-defined data governance framework helps organizations mitigate risks associated with data misuse and enhances decision-making by providing reliable data. When evaluating data governance strategies, it is essential to consider the alignment of data management practices with business objectives, the involvement of stakeholders across various departments, and the integration of technology solutions that facilitate data stewardship. The scenario presented in the question requires an understanding of how these elements interact and the implications of governance decisions on data integrity and organizational performance.

Data migration strategies are crucial for ensuring that data is transferred accurately and efficiently from one system to another. In the context of Oracle Fusion Data Intelligence, understanding the nuances of different migration approaches is essential for successful implementation. One common strategy is the “big bang” approach, where all data is migrated at once during a scheduled downtime. This method can be efficient but poses risks, such as potential data loss or system downtime if issues arise. Alternatively, a phased migration allows for gradual data transfer, which can minimize disruption but may complicate the integration process. When considering a migration strategy, factors such as data volume, system compatibility, and business continuity must be evaluated. For instance, a company with a large volume of critical data may opt for a phased approach to ensure that each segment is validated before proceeding. Additionally, organizations must consider the impact on users and operations during the migration process. A well-defined strategy not only addresses technical aspects but also involves stakeholder communication and training to ensure a smooth transition. Ultimately, the choice of migration strategy should align with the organization’s goals, the complexity of the data environment, and the resources available for the migration project.

In the context of Oracle Fusion Applications, integration is a critical aspect that enables seamless data flow and operational efficiency across various business functions. When integrating with Oracle Fusion Applications, it is essential to understand the various methods and tools available for data exchange, such as REST APIs, SOAP web services, and Oracle Integration Cloud. Each method has its own strengths and weaknesses, and the choice of integration method can significantly impact the performance and reliability of the data exchange process. For instance, REST APIs are often preferred for their simplicity and ease of use, particularly in modern web applications, while SOAP web services may be utilized for more complex transactions requiring higher security and reliability. Additionally, Oracle Integration Cloud provides a comprehensive platform for orchestrating integrations, allowing for the automation of workflows and the transformation of data between different systems. Understanding these integration methods and their appropriate applications is crucial for professionals working with Oracle Fusion Data Intelligence. It allows them to design effective integration strategies that align with business objectives and ensure that data is accurately and efficiently shared across the organization.

In data processing optimization, understanding the efficiency of algorithms is crucial. Consider a scenario where a data processing task requires sorting a dataset of size $n$. The time complexity of a sorting algorithm can be expressed as $T(n) = O(n \log n)$ for efficient algorithms like mergesort or heapsort. However, if we use a less efficient algorithm, such as bubble sort, the time complexity is $T(n) = O(n^2)$. To optimize the data processing, we can analyze the performance of these algorithms under different conditions. For instance, if we have a dataset that is already partially sorted, we might choose an algorithm with a best-case scenario of $O(n)$, such as insertion sort. Now, let’s consider a practical example: if we have a dataset of size $n = 1000$, the time taken by mergesort can be approximated as: $$ T(n) = c \cdot n \log n = c \cdot 1000 \log_2(1000) $$ Assuming $c = 1$, we can calculate: $$ T(1000) \approx 1000 \cdot 10 = 10000 \text{ operations} $$ In contrast, for bubble sort: $$ T(n) = c \cdot n^2 = c \cdot 1000^2 = c \cdot 1000000 $$ Thus, if $c = 1$, it would take approximately $1000000$ operations. This stark difference illustrates the importance of selecting the right algorithm based on the dataset characteristics and the desired optimization.

Logical data models are essential in the realm of data architecture as they provide a structured framework for understanding how data elements relate to one another within a system. They abstract the physical aspects of data storage and focus on the relationships and constraints that govern data integrity and usability. In the context of Oracle Fusion Data Intelligence, a logical data model serves as a blueprint that guides the design of databases and data warehouses, ensuring that data is organized in a way that supports business processes and analytics. When developing a logical data model, it is crucial to identify entities, attributes, and relationships accurately. Entities represent real-world objects or concepts, attributes describe the properties of these entities, and relationships define how entities interact with one another. A well-constructed logical data model not only enhances data consistency and accuracy but also facilitates better communication among stakeholders, including business analysts, data architects, and developers. In practice, a logical data model can help identify potential data quality issues and inform decisions about data governance and management. It is also instrumental in ensuring that the data architecture aligns with business requirements and supports future scalability. Therefore, understanding the nuances of logical data models is vital for professionals working with Oracle Fusion Data Intelligence.

In the realm of data management, understanding the differences between Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP) is crucial for implementing effective data solutions. OLAP systems are designed for complex queries and data analysis, allowing users to perform multidimensional analysis of business data. They are optimized for read-heavy operations, enabling users to generate reports and insights from large volumes of historical data. In contrast, OLTP systems are focused on managing transactional data, supporting day-to-day operations with a high volume of short online transactions. They are optimized for write-heavy operations, ensuring data integrity and quick response times for user transactions. When considering the implementation of a data intelligence solution, it is essential to recognize the specific use cases for OLAP and OLTP. For instance, a retail company may use OLTP systems to handle sales transactions in real-time, while OLAP systems would be employed to analyze sales trends over time, helping to inform strategic decisions. Understanding these distinctions allows professionals to design systems that leverage the strengths of each approach, ensuring that data is processed efficiently and effectively for both operational and analytical purposes.

In the context of data warehouse architecture, understanding the various components and their interactions is crucial for effective implementation and management. A data warehouse typically consists of several layers, including the data source layer, staging area, data integration layer, and presentation layer. Each layer serves a specific purpose, such as data extraction, transformation, loading (ETL), and providing access to end-users for reporting and analysis. The architecture must also consider scalability, performance, and data governance. In this scenario, the focus is on identifying the correct architecture that supports a robust data warehouse environment, particularly in terms of how data flows through the system and how it is structured for optimal querying and reporting. The correct answer will reflect an understanding of these principles and their application in real-world scenarios.

Forecasting techniques are essential in data intelligence as they allow organizations to predict future trends based on historical data. In the context of Oracle Fusion Data Intelligence, understanding how to apply various forecasting methods is crucial for making informed business decisions. One common technique is time series forecasting, which analyzes data points collected or recorded at specific time intervals. This method is particularly useful for identifying seasonal patterns and trends over time. Another approach is causal forecasting, which considers the relationship between the variable being forecasted and other influencing factors. For instance, sales forecasts may depend on marketing spend, economic indicators, or competitor actions. In practice, selecting the appropriate forecasting technique depends on the nature of the data and the specific business context. For example, if a company is looking to forecast demand for a seasonal product, time series analysis might be more effective. Conversely, if the goal is to understand how changes in pricing affect sales, causal forecasting would be more suitable. Understanding these nuances is vital for implementing effective forecasting strategies within Oracle Fusion Data Intelligence, as it directly impacts resource allocation, inventory management, and overall strategic planning.

In the realm of data analytics, understanding the implications of data visualization techniques is crucial for effective communication of insights. When analyzing data, one must consider not only the type of data being presented but also the audience’s ability to interpret that data. For instance, using complex visualizations such as 3D graphs or intricate heat maps may overwhelm stakeholders who are not data-savvy, leading to misinterpretation of the findings. Conversely, simpler visualizations like bar charts or line graphs can effectively convey trends and comparisons without unnecessary complexity. The choice of visualization should align with the data’s narrative and the audience’s familiarity with the subject matter. Additionally, the context in which the data is presented can significantly influence decision-making processes. A well-chosen visualization can highlight key insights, making it easier for stakeholders to grasp the implications of the data and make informed decisions. Therefore, understanding the audience’s needs and the context of the data is essential for selecting the appropriate visualization technique.

Data migration strategies are critical in ensuring that data is accurately and efficiently transferred from one system to another, particularly in complex environments like Oracle Fusion Data Intelligence. A well-defined migration strategy considers various factors, including data integrity, system compatibility, and the specific needs of the organization. One common approach is the “big bang” migration, where all data is moved at once during a planned downtime. This method can be efficient but carries risks, such as potential data loss or system downtime if issues arise. Alternatively, a phased migration allows for gradual data transfer, which can minimize disruption but may complicate the integration process. Understanding the nuances of these strategies is essential for professionals tasked with implementing data migration in Oracle Fusion environments. Additionally, the choice of strategy can impact the overall success of the implementation, as it affects not only the technical aspects but also user adoption and system performance post-migration. Therefore, professionals must critically evaluate the specific context of their organization and the data involved to select the most appropriate migration strategy.

In the realm of machine learning, understanding the nuances of different algorithms is crucial for effective data analysis and predictive modeling. Each algorithm has its strengths and weaknesses, and the choice of algorithm can significantly impact the performance of a model. For instance, decision trees are known for their interpretability and ease of use, making them suitable for scenarios where model transparency is essential. However, they can be prone to overfitting, especially with complex datasets. On the other hand, algorithms like support vector machines (SVM) are powerful for classification tasks, particularly in high-dimensional spaces, but they require careful tuning of parameters and can be less interpretable. In this context, it is essential to evaluate the suitability of different algorithms based on the specific characteristics of the dataset and the problem at hand. For example, if a dataset has a large number of features but relatively few samples, algorithms that can handle high dimensionality, such as SVM or regularized regression techniques, may be more appropriate. Conversely, if the dataset is large and complex, ensemble methods like random forests or gradient boosting may provide better performance by combining the strengths of multiple models. Ultimately, the decision on which machine learning algorithm to implement should be guided by a thorough understanding of the data, the problem requirements, and the trade-offs associated with each algorithm.

Predictive analytics is a crucial aspect of data intelligence, particularly in the context of Oracle Fusion applications. It involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on past events. In a business scenario, predictive analytics can help organizations forecast sales, manage inventory, and enhance customer experiences by anticipating needs. The effectiveness of predictive analytics relies heavily on the quality of data and the appropriateness of the models used. For instance, a retail company might analyze customer purchase history to predict future buying behavior, allowing for targeted marketing strategies. However, the success of these predictions can be influenced by various factors, including seasonality, market trends, and external economic conditions. Understanding the nuances of predictive analytics, including the selection of relevant variables and the interpretation of model outputs, is essential for making informed decisions. This question tests the ability to apply predictive analytics concepts in a practical scenario, requiring a deep understanding of how different factors can influence predictive outcomes.

Oracle Data Integrator (ODI) is a comprehensive data integration platform that enables organizations to efficiently manage and transform data across various sources and targets. One of the key features of ODI is its ability to utilize knowledge modules (KMs), which are reusable components that define how data is extracted, transformed, and loaded (ETL). Understanding the role of KMs is crucial for implementing effective data integration solutions. In this context, the choice of the appropriate KM can significantly impact the performance and efficiency of data integration processes. Additionally, ODI supports various integration patterns, including batch processing and real-time data integration, which can be tailored to meet specific business requirements. The ability to configure and optimize these processes requires a nuanced understanding of both the technical capabilities of ODI and the business context in which it operates. Therefore, when faced with a scenario involving the selection of a KM for a specific data integration task, it is essential to consider factors such as data volume, transformation complexity, and performance requirements to make an informed decision.

In the context of Oracle Fusion Data Intelligence, model deployment and management are critical components that ensure the effective utilization of machine learning models in production environments. When deploying a model, it is essential to consider various factors such as scalability, performance monitoring, and integration with existing systems. A well-deployed model should not only function correctly but also adapt to changes in data patterns over time. This requires a robust management strategy that includes version control, rollback capabilities, and continuous monitoring to assess model performance against key performance indicators (KPIs). Additionally, understanding the implications of model drift—where the statistical properties of the input data change over time—can significantly impact the model’s effectiveness. Therefore, organizations must implement strategies for retraining and updating models as necessary. The question presented here tests the understanding of these concepts by presenting a scenario that requires the application of knowledge regarding model deployment and management practices.

Change management in data projects is a critical aspect that ensures the successful implementation and adoption of new systems and processes. It involves preparing, supporting, and helping individuals, teams, and organizations in making organizational change. In the context of data projects, effective change management can significantly influence the project’s outcome by addressing the human side of change. This includes understanding the impact of new data systems on users, providing adequate training, and ensuring that stakeholders are engaged throughout the process. A well-structured change management plan can help mitigate resistance, enhance user acceptance, and ultimately lead to a more successful implementation of data initiatives. It is essential to recognize that change is not merely a technical challenge but also a cultural and behavioral one. Therefore, strategies such as communication, training, and stakeholder involvement are vital to facilitate a smooth transition. By focusing on these elements, organizations can better navigate the complexities of change and achieve their data project goals.

Performance tuning in Oracle Cloud is a critical aspect of ensuring that applications run efficiently and effectively. It involves optimizing various components such as database queries, application code, and infrastructure settings to enhance overall performance. One key area of focus is the identification of bottlenecks, which can occur at different layers of the architecture, including the database, application server, and network. Techniques such as indexing, query optimization, and load balancing are commonly employed to improve response times and resource utilization. Additionally, understanding the workload patterns and resource consumption can help in making informed decisions about scaling resources up or down based on demand. Furthermore, leveraging Oracle’s built-in monitoring tools can provide insights into performance metrics, allowing for proactive adjustments before issues escalate. In a cloud environment, where resources are often shared, it is essential to consider the impact of other tenants on performance and to implement strategies that mitigate these effects. Overall, effective performance tuning requires a comprehensive understanding of both the technical aspects and the business requirements to ensure that the system meets user expectations while maintaining cost efficiency.

Data security principles are fundamental to protecting sensitive information within any organization, especially in environments that utilize advanced data intelligence solutions like Oracle Fusion. One of the core principles is the concept of least privilege, which dictates that users should only have access to the information and resources necessary for their specific roles. This minimizes the risk of unauthorized access and potential data breaches. Another important principle is data encryption, which ensures that data is rendered unreadable to unauthorized users, thus protecting it during storage and transmission. Additionally, regular audits and monitoring are essential to identify and respond to potential security threats proactively. Understanding these principles is crucial for implementing effective data security measures in Oracle Fusion Data Intelligence, as they help maintain compliance with regulations and safeguard organizational data against evolving threats.

Data security principles are fundamental to ensuring the integrity, confidentiality, and availability of data within any organization. In the context of Oracle Fusion Data Intelligence, understanding these principles is crucial for implementing effective data governance and protection strategies. One of the key principles is the concept of least privilege, which dictates that users should only have access to the data necessary for their job functions. This minimizes the risk of unauthorized access and potential data breaches. Another important principle is data encryption, which protects sensitive information both at rest and in transit, ensuring that even if data is intercepted, it remains unreadable without the appropriate decryption keys. Additionally, regular audits and monitoring are essential to detect any anomalies or unauthorized access attempts, allowing organizations to respond swiftly to potential threats. Understanding these principles not only helps in compliance with regulations but also fosters a culture of security awareness within the organization. Therefore, when evaluating scenarios related to data security, it is important to consider how these principles are applied and the implications of their implementation on overall data governance.

In the context of implementing Oracle Fusion Data Intelligence, best practices are crucial for ensuring a successful deployment and adoption of the system. One of the key best practices is to establish a clear governance framework that outlines roles, responsibilities, and processes for data management. This framework helps in maintaining data quality, security, and compliance with regulations. Additionally, engaging stakeholders early in the implementation process is vital. This includes gathering requirements, understanding user needs, and ensuring that the solution aligns with business objectives. Another important aspect is to prioritize training and change management. Users must be adequately trained to utilize the new system effectively, and change management strategies should be in place to facilitate a smooth transition. Lastly, continuous monitoring and feedback loops should be established to assess the system’s performance and make necessary adjustments. By adhering to these best practices, organizations can maximize the value derived from their Oracle Fusion Data Intelligence implementation and ensure long-term success.

In this question, we are tasked with analyzing a dataset that follows a normal distribution. The mean ($\mu$) of the dataset is given as 50, and the standard deviation ($\sigma$) is 10. We need to determine the probability that a randomly selected data point falls within one standard deviation of the mean. In a normal distribution, approximately 68% of the data falls within one standard deviation of the mean. This can be mathematically expressed as: $$ P(\mu – \sigma < X < \mu + \sigma) $$ Substituting the values of $\mu$ and $\sigma$, we have: $$ P(50 – 10 < X < 50 + 10) $$ This simplifies to: $$ P(40 < X < 60) $$ To find this probability, we can use the properties of the normal distribution. The area under the curve between $40$ and $60$ represents the probability that a randomly selected data point falls within this range. Since we know that approximately 68% of the data lies within one standard deviation of the mean in a normal distribution, we can conclude that: $$ P(40 < X < 60) \approx 0.68 $$ Thus, the probability that a randomly selected data point falls within one standard deviation of the mean is approximately 0.68 or 68%.