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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance, Quality, and Management | 14% | - Data privacy, security, and compliance - Data lifecycle management and documentation - Data quality standards and monitoring - Data governance frameworks and policies |
| Topic 2: Data Acquisition and Preparation | 22% | - Data extraction, transformation, and loading (ETL/ELT) - Data cleaning, validation, and quality improvement - Data integration and normalization - Data collection methods and sources |
| Topic 3: Visualization and Reporting | 20% | - Dashboard and report development - Communicating insights to stakeholders - Validation and accuracy of reports - Design principles for effective visualizations |
| Topic 4: Data Concepts and Environments | 20% | - Foundational concepts of AI and machine learning in data - Database systems and architectures - Cloud and distributed data environments - Data types, structures, and formats |
| Topic 5: Data Analysis | 24% | - Analytical tools and querying languages - Exploratory and confirmatory data analysis - Data modeling and pattern recognition - Statistical methods and techniques |
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NEW QUESTION # 115
A data analyst has a dashboard that shows weekly data. For the past few weeks, the data has not updated.
Which of the following is the best way to confirm that the data is current?
Answer: C
NEW QUESTION # 116
Which of the following AI types is the best option for time-series forecasting?
Answer: C
Explanation:
Foundational models are large AI models trained on vast amounts of data, often exhibiting strong generalization capabilities. While not specifically architected for time-series, their ability to learn complex patterns could potentially be leveraged for forecasting tasks through fine-tuning or specialized architectures built upon them.
In reality, the best AI types specifically designed for time-series forecasting include:
* Recurrent Neural Networks (RNNs), especially LSTMs and GRUs:These architectures are designed to handle sequential data and capture temporal dependencies.
* Transformer Networks:Originally developed for NLP, Transformers have shown remarkable success in time-series forecasting due to their ability to capture long-range dependencies.
* Traditional statistical models:ARIMA, Exponential Smoothing, and other statistical methods remain powerful and interpretable options for time-series analysis.
Therefore, while "foundational models" have some potential, it's important to understand that they aren't the primary or specifically designed AI type for time-series forecasting.
NEW QUESTION # 117
Which of the following data structures would a data engineer most likely receive when using a get command?
Answer: A
Explanation:
JSON (JavaScript Object Notation) is a common data structure returned by GET requests in APIs and web services. It provides a lightweight, structured format for exchanging data between systems and is widely used for transmitting records, objects, and arrays.
NEW QUESTION # 118
Given the following cloud service provider data:
Which of the following is the best way to represent the information?
Answer: A
Explanation:
A donut chart is ideal for displaying parts of a whole when the values represent percentages of a total. The cloud providers' market share percentages add up to 100%, making a donut chart an effective way to show each provider's proportion of the overall market.
NEW QUESTION # 119
A data analyst needs to create a report that anticipates the number of calls received daily. Which of the following is the best statistical method to use?
Answer: A
Explanation:
Predictive analysis uses historical data and statistical models to forecast future outcomes. Since the goal is to anticipate the number of calls that will be received each day, predictive analysis is the most appropriate method.
NEW QUESTION # 120
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