SDS Dumps With Exact Questions and Answers

Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model by maximizing the likelihood function — i.e., finding the parameters that make the observed data most probable.

Option A: Correct. MLE provides a framework for a large class of problems in data science, including regression, classification, generative models, and probabilistic inference.

Option B: Incorrect — it applies to many problems, not just a small subset.

Option C & D: Incorrect. HDFS (Hadoop Distributed File System) is a storage technology, unrelated to MLE.

Option E: Incorrect because C is invalid.

Thus, the correct answer is Option A (Large class of problems in Data Science).

Pandas is one of the most widely used Python libraries for data analysis and manipulation. Its central object is the DataFrame.

Option A: Correct. DataFrame is the core data structure in Pandas.

Option B: Correct. DataFrame resembles SQL tables and R data frames, supporting row/column indexing, joins, and grouping.

Option C: Correct. A DataFrame is essentially a 2D labeled table consisting of rows and columns.

Option D: Correct, but not fully inclusive.

Option E: Correct, since all of A, B, and C are true.

Thus, the best answer is Option E (All of the above).

Exploratory analytics (often referred to as Exploratory Data Analysis – EDA) is a fundamental step in data science, enabling practitioners to discover initial insights, detect anomalies, and understand the structure of datasets before applying predictive or prescriptive modeling.

Option A (Understand the data content): Correct. EDA techniques (descriptive statistics, summary tables, profiling) reveal missing values, data types, and distributions.

Option B (Gain a high-level understanding of relationships): Correct. Correlation analysis, scatter plots, and cross-tabulations help identify dependencies between variables.

Option C (Understand patterns in the data): Correct. Visualization and clustering methods help discover hidden structures, seasonalities, and outliers.

Since exploratory algorithms contribute to all of these objectives, the correct answer is Option E (All of the above).