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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis | 23% | - Data interpretation and reporting - Trend and correlation analysis - Statistical methods and calculations |
| Topic 2: Visualization | 23% | - Chart and dashboard creation - Visualization best practices - Tool-based visualization techniques |
| Topic 3: Data Mining | 25% | - Data transformation and profiling - Pattern identification and extraction - Data acquisition and cleaning techniques |
| Topic 4: Data Governance, Quality, and Controls | 14% | - Data quality management - Security, privacy, and compliance considerations - Data governance frameworks |
| Topic 5: Data Concepts and Environments | 15% | - Data concepts and terminology - Data sources and acquisition methods - Data structures and file formats |
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NEW QUESTION # 85
Andy is a pricing analyst for a retailer. Using a hypothesis test, he wants to assess whether people who receive electronic coupons spend more on average.
What should Andy's null hypothesis be?
Answer: B
Explanation:
Explanation
The null hypothesis presumes the status quo. Andy is testing whether or not people who receive an electronic coupon spend more on average, so, the null hypothesis states that people who receive the coupon do spend more on average.
NEW QUESTION # 86
In a tabular dataset, what term is used to describe the data that appears in a single row?
Answer: C
NEW QUESTION # 87
An analyst is working with a data set that lists individuals' first and last names in separate columns. Which of the following processes should the analyst use to combine the first and last names into a single spreadsheet cell?
Answer: C
NEW QUESTION # 88
An analysts building a monthly report for production and wants to ensure the audience is aware of its once-a-month cadence. Which of the following is the MOST important to convey that information?
Answer: C
NEW QUESTION # 89
Given the diagram below:
Which of the following steps is missing?
Answer: B
Explanation:
The missing step in the Extract, Transform, Load (ETL) process is typically the cleaning step, which involves removing redundant data or deduplication. This step is crucial in the ETL process to ensure that the data loaded into the destination is accurate and not inflated by duplicate records. The other options, like validating data types and connecting to the data API, are important but do not fit into the standard ETL process steps as a cleaning operation. Normalizing the data is part of the 'Transform' step, which was already listed.
NEW QUESTION # 90
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