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NEW QUESTION # 87
Which characteristics are typically associated with unstructured data formats?
(Choose two)
- A. Lacks predefined data schema
- B. Requires parsing or transformation before analysis
- C. Stored using fixed-width fields and records
- D. Highly tabular and query-friendly
Answer: A,B
NEW QUESTION # 88
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?
- A. .loc[] can only be used on Series
- B. .loc[] does not allow slicing
- C. .iloc[] allows label-based selection
- D. .iloc[] is position-based, while .loc[] is label-based
Answer: D
NEW QUESTION # 89
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?
- A. Custom functions
- B. Global variables
- C. Tuple unpacking
- D. Lambda functions
Answer: A
NEW QUESTION # 90
When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)
- A. dropna
- B. ignore_index
- C. suffixes
- D. on
Answer: C,D
NEW QUESTION # 91
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?
- A. A strong negative linear relationship
- B. No correlation
- C. A moderate non-linear association
- D. A weak positive relationship
Answer: A
NEW QUESTION # 92
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)
- A. Features with different units and scales
- B. Presence of duplicate data
- C. Boolean feature encoding
- D. Distance-based modeling algorithms
Answer: A,D
NEW QUESTION # 93
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?
- A. Use drop() to remove null rows
- B. Use fillna() with a constant value
- C. Use dropna() with axis=1
- D. Apply interpolate() method
Answer: B
NEW QUESTION # 94
Which data collection method is most appropriate when gathering opinions from a target audience at scale?
- A. Surveys
- B. Sensor logs
- C. Transactional data
- D. Web scraping
Answer: A
NEW QUESTION # 95
Which practices help ensure secure and effective execution of SQL queries in Python scripts?
(Choose all that apply)
- A. Employing parameterized statements for user-supplied data
- B. Wrapping SQL commands in try-except blocks
- C. Using string formatting to build SQL queries dynamically
- D. Using string concatenation to insert variables into SQL
Answer: A,B
NEW QUESTION # 96
What is the primary advantage of using web scraping as a data collection method in business analytics?
- A. It eliminates the need for data cleaning
- B. It ensures compliance with privacy laws
- C. It allows automation of data collection from public sources
- D. It guarantees real-time data accuracy
Answer: C
NEW QUESTION # 97
When analyzing a dataset of customer ages, you calculate the mean, median, and mode. What does it indicate if all three values are approximately equal?
- A. The dataset has a normal distribution
- B. The dataset contains outliers
- C. The dataset is uniformly distributed
- D. The dataset is positively skewed
Answer: A
NEW QUESTION # 98
How would you extract the last three rows of a DataFrame df using position-based indexing?
- A. df.tail(3)
- B. df.iloc[-3:]
- C. df.loc[-3:]
- D. df[-3]
Answer: B
NEW QUESTION # 99
Which method is commonly used to handle missing numerical data in a dataset?
- A. Dropping all columns
- B. Replacing with text labels
- C. Filling with the mean or median
- D. Multiplying all missing values by zero
Answer: C
NEW QUESTION # 100
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?
- A. It configures multiple plots in a grid layout within a single figure
- B. It overlays multiple plots in the same figure without axes
- C. It links Matplotlib with Seaborn's grid styling
- D. It creates a 3D surface plot using pandas
Answer: A
NEW QUESTION # 101
What is the best approach when comparing SQL NULL values with Python data during conditional checks?
- A. Convert all NULL to string before comparison
- B. Use Python's None and SQL's IS NULL clause
- C. Use == None directly in SQL queries
- D. Replace NULL with zero before importing
Answer: B
NEW QUESTION # 102
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