SDS Actual Questions Answers PDF 100% Cover Real Exam Questions
SDS Exam questions and answers
NEW QUESTION # 24
What is TRUE for "rehashing"?
- A. It requires a new hash function, which maps values into a larger range of integers
- B. All of the above
- C. Key/value pairs from the original table can be inserted into the new, larger one
- D. Allocate a new, larger hash table in memory
- E. Both A and B
Answer: B
Explanation:
Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.
Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.
Option B (Correct): A new hash function is typically required to map keys into the expanded table range.
Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.
Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Data Structures & Hashing Techniques
NEW QUESTION # 25
Which of the following architectural techniques is used for parallel processing?
- A. Both B and C
- B. The Superscalar Technique
- C. Very Long Instruction Words (VLIW) Technique
- D. The SuperVector Technique
- E. Both A and B
Answer: E
Explanation:
Parallel processing architectures are designed to execute multiple instructions or operations simultaneously:
Superscalar Technique (Option A): Uses multiple execution units so that several instructions can be issued and executed in parallel within a single CPU cycle.
VLIW Technique (Option B): Uses very long instruction words, where multiple operations are encoded into a single instruction and executed in parallel.
SuperVector (Option C): Refers to vector processors, which process large arrays of data but is not classified as a mainstream architectural parallel technique in modern CPU design.
Therefore, the primary architectural techniques for parallel processing are Superscalar and VLIW, making Option D (Both A and B) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Parallel and Distributed Processing.
NEW QUESTION # 26
Which of the following is True about Time Series Analysis?
- A. Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
- B. Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
- C. Predicting when/whether an event will occur, such as a failure of the machine generating the data
- D. All of the above
- E. Both A and B
Answer: D
Explanation:
Time Series Analysis (TSA) is the process of analyzing data collected sequentially over time to extract meaningful insights.Applications include:
Option A: Correct. Event prediction (e.g., failure detection in IoT or predictive maintenance).
Option B: Correct. Forecasting future values (e.g., stock price, sales forecasting).
Option C: Correct. Pattern discovery in large-scale time series datasets using clustering, anomaly detection, or seasonality detection.
Since all three are true, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Time Series Analysis and Forecasting.
NEW QUESTION # 27
Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized
- A. i, ii
- B. i, iii
- C. ii, iii
- D. None of the above
Answer: A
Explanation:
Images are broadly categorized based on how they store visual information:
Rasterized images (Option i):
Composed of a grid of pixels (bitmap).
Each pixel has color information.
Examples: JPEG, PNG, BMP.
Best for photos or complex visuals.
Vectorized images (Option ii):
Composed of paths defined by mathematical formulas.
Scalable without quality loss.
Examples: SVG, EPS, AI.
Best for logos, icons, and illustrations.
Sectorized images (Option iii):
Not a standard category in computer graphics.
Thus, image files are categorized into Rasterized and Vectorized, making Option A (i, ii) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Types & Multimedia Data Management.
NEW QUESTION # 28
Which of the following is the common evolutionary algorithm of neural networks?
- A. Evolution strategy
- B. All of the above
- C. Genetic algorithm
- D. Genetic programming
Answer: B
Explanation:
Evolutionary algorithms are optimization techniques inspired by natural selection. They are often applied in neural network optimization for evolving architectures, weights, or hyperparameters.
Genetic Algorithm (Option A): Uses selection, crossover, and mutation to evolve neural network parameters.
Genetic Programming (Option B): Evolves computer programs or expressions; can be applied to evolve neural network structures.
Evolution Strategy (Option C): Focuses on optimization using mutation and adaptation of parameters.
Since all are used as evolutionary approaches in neural networks, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Neural Networks: Evolutionary Algorithms in Machine Learning.
NEW QUESTION # 29
In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:
- A. Detecting irregularities
- B. Detecting regularities
- C. None of the above
- D. Both A and B
Answer: D
Explanation:
Unsupervised learning is a machine learning approach where no labeled outputs are provided. The algorithm discovers patterns or structures directly from raw data.
Option A (Detecting regularities): Correct. Unsupervised learning identifies hidden structures such as clusters, associations, and dimensionality reductions (e.g., k-means clustering, PCA).
Option B (Detecting irregularities): Correct. Outlier detection is also a part of unsupervised learning, often used in anomaly detection (e.g., fraud detection, intrusion detection).
Option C: Correct, since unsupervised learning helps detect both regularities (clusters, groups) and irregularities (outliers, anomalies).
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Unsupervised Learning: Clustering, Anomaly Detection, and Pattern Discovery.
NEW QUESTION # 30
What is Scrumban?
- A. It is Scrum
- B. It combines the principles of Scrum and Kanban into a pull-based system
- C. It is Kanban
- D. It combines the principles of Scrum and Kanban into a push-based system
Answer: B
Explanation:
Scrumban is a hybrid Agile methodology that merges Scrum and Kanban to take advantage of the strengths of both.
From Scrum, Scrumban adopts structured sprint planning, roles, and iterative review cycles.
From Kanban, it borrows the visual board system, continuous workflow management, and the pull-based approach, where tasks are pulled into the workflow only when capacity is available.
The pull-based system ensures that teams do not overload themselves and helps manage work-in-progress (WIP) effectively. This makes Scrumban particularly suitable for projects with frequent changes, ongoing maintenance tasks, or teams transitioning from Scrum to Kanban.
Thus, the correct answer is Option C.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Project Management Techniques for Data Science.
NEW QUESTION # 31
Example of amortized performance is:
- A. MapReduce dictionaries
- B. Hadoop dictionaries
- C. HDFS dictionaries
- D. Python dictionaries
- E. All of the above
Answer: D
Explanation:
Amortized performance refers to averaging the cost of operations over a sequence of actions, ensuring that while some operations may be costly, the overall average time per operation remains efficient.
Python Dictionaries (Option B): Implemented using hash tables. Insertions, deletions, and lookups typically run in O(1) average time, but occasionally require rehashing (costly). The high cost of rehashing is spread over many operations, giving amortized constant-time performance.
Option A (Hadoop dictionaries): Not standard terminology.
Option C (HDFS dictionaries): HDFS doesn't use dictionary structures in this sense.
Option D (MapReduce dictionaries): MapReduce uses key-value pairs, but amortized dictionary performance is not its focus.
Thus, the correct answer is Option B (Python dictionaries).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Hash Tables & Amortized Analysis.
NEW QUESTION # 32
Which of the following is NOT a part of Internal Process Optimization?
- A. Business Monitoring
- B. Business Insights
- C. Business Optimization
- D. None of the above
- E. Business Metamorphosis
Answer: E
Explanation:
Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.
Typical components of IPO include:
Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.
Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.
Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.
However:
Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.
Therefore, the correct answer is Option B (Business Metamorphosis).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Internal Process Optimization.
NEW QUESTION # 33
The DevOps movement is an outgrowth of which of the following software development methodologies?
- A. Agile
- B. Test-driven development and model-driven development
- C. Waterfall
- D. Promise-based algorithms
Answer: A
Explanation:
The DevOps movement evolved as a natural extension of the Agile methodology.
Agile (Option A): Agile emphasizes iterative development, collaboration, and flexibility. While Agile improved software development speed, it created challenges in integrating development with IT operations.
DevOps emerged to address this by bringing operations into the Agile cycle - enabling continuous integration, delivery, and deployment.
Waterfall (Option B): Incorrect. Waterfall is a rigid, sequential methodology, fundamentally opposite to the DevOps philosophy.
Promise-based algorithms (Option C): Not a methodology - irrelevant here.
Test-driven development and model-driven development (Option D): While these practices support DevOps, they are not the origin of the movement.
Thus, the DevOps movement is an outgrowth of Agile methodology.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile and DevOps in Data Science Projects.
NEW QUESTION # 34
Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?
- A. Scatter Plot
- B. Histogram
- C. Trend Analysis
- D. Box Plots
- E. None of the above
Answer: D
Explanation:
Box plots (or Whisker plots) are statistical graphics that represent data distribution through:
Minimum, First Quartile (Q1), Median, Third Quartile (Q3), and Maximum.
Outliers are plotted as individual points beyond the whiskers.
This makes them particularly powerful for:
Identifying outliers in data.
Comparing distributions across categories.
Understanding variability in data.
Option A (Trend Analysis): Shows temporal patterns, not individual outliers.
Option C (Histogram): Shows frequency distribution but does not explicitly highlight outliers.
Option D (Scatter Plot): Shows relationships between variables but doesn't focus on statistical outliers in one distribution.
Thus, the correct answer is Option B (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots for Outlier Detection.
NEW QUESTION # 35
ElementTree sub-library gives us direct access to:
- A. Delete tree of the XML
- B. Parse tree of the XML
- C. Insert tree of the XML
- D. Copy tree of the XML
- E. None of the above
Answer: B
Explanation:
In Python, the ElementTree module (part of the standard library xml.etree.ElementTree) provides a simple and efficient API for parsing and creating XML data.
The main feature of ElementTree is its ability to provide direct access to the parse tree of an XML document.
This allows developers to:
Parse XML into an in-memory tree structure.
Traverse, search, modify, and extract information from XML elements.
Write back changes into XML files.
Options B, C, and D (Delete, Copy, Insert tree) are not standard terminology in XML handling with ElementTree. While you can delete, insert, or copy elements, the module itself primarily gives parse tree access.
Thus, the correct answer is Option A (Parse tree of the XML).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: XML/JSON Handling in Python.
NEW QUESTION # 36
Which of the following statements is correct?
- A. Apache claimed that Spark is able to run parallel jobs 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce
- B. Apache claimed that Spark is able to run parallel jobs 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce
- C. Apache claimed that Spark is able to run parallel jobs 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
- D. Apache claimed that Spark is able to run parallel jobs 1000 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
Answer: A
Explanation:
Apache Spark is a distributed computing framework designed as an improvement over Hadoop's MapReduce.
According to the official Apache Spark documentation:
Spark can run workloads up to 100x faster in memory.
Spark can run workloads up to 10x faster on disk.
This performance gain comes from Spark's use of in-memory computation, DAG execution engine, and optimized query execution, compared to the slower, disk-heavy Hadoop MapReduce framework.
Thus, the correct statement is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Spark vs Hadoop Performance Comparisons.
NEW QUESTION # 37
Which of the following is NOT Structured data?
- A. XML
- B. Relational databases
- C. Data tables
- D. CSV files
- E. None of the above
Answer: A
Explanation:
Structured data refers to highly organized data stored in predefined formats such as rows and columns.
Relational databases (Option A): Structured.
Data tables (Option B): Structured.
CSV files (Option C): Structured because they are tabular with rows and columns.
XML (Option D): Not structured but semi-structured because it stores hierarchical information with tags and attributes.
Thus, the correct answer is Option D (XML).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Structured vs Semi- structured Data.
NEW QUESTION # 38
Which of the following is NOT a main data container in Python?
- A. Dict
- B. Lists
- C. LinkedList
- D. Tuples
Answer: C
Explanation:
Python's built-in data containers are widely used in data science and programming:
Lists (Option A): Ordered, mutable sequences.
Tuples (Option B): Ordered, immutable sequences.
Dict (Option D): Dictionaries, key-value data structures implemented using hash tables.
LinkedList (Option C): Python does not provide a native linked list as a built-in container. While linked lists can be implemented manually or through external libraries (e.g., collections.deque for efficient appends
/pops), they are not part of Python's main native containers.
Thus, the correct answer is Option C (LinkedList).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Python Data Structures.
NEW QUESTION # 39
Semi-structured data does NOT include:
- A. Schema-full data
- B. File systems
- C. Scientific data
- D. Database system
Answer: A
NEW QUESTION # 40
Which of the following is NOT a valid section of Big Data Strategy document?
- A. Key business entities
- B. Business decisions
- C. Key decisions
- D. Key business initiatives
- E. Business strategy
Answer: B
Explanation:
A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:
Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.
Key decisions (Option B): Identifies the decisions data will help optimize or automate.
Key business initiatives (Option D): Links big data projects with critical organizational initiatives.
Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.
However:
Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.
Thus, the correct answer is Option C (Business decisions).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Strategy and Business Alignment.
NEW QUESTION # 41
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