Certification Training for SDS Exam Dumps Test Engine [2026]

Jun 16, 2026 Step by Step Guide to Prepare for SDS Exam

NEW QUESTION # 52
Which of the following is a trend analysis component of time series decomposition?

• A. Irregular
• B. Both A and B
• C. Seasonal
• D. All of the above
• E. Cyclical

Answer: D

Explanation:
Time series decomposition breaks down data into components to better understand underlying patterns and support forecasting. The main components are:
Trend: Long-term progression (upward or downward).
Seasonal: Repeating short-term patterns (e.g., monthly or quarterly).
Cyclical (Option A): Medium- to long-term cycles (e.g., business cycles).
Irregular/Residual (Option C): Random, unpredictable variations.
Since trend analysis involves examining cyclical, seasonal, and irregular components, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Decomposition and Trend Analysis.

NEW QUESTION # 53
Which of the following is a Python library for fitting Bayesian networks to real data?

• A. MCMC
• B. SciLib
• C. SCIMC
• D. MyLib
• E. PyMC

Answer: E

Explanation:
The correct answer isPyMC(Option B).
PyMC is an open-source Python library widely used forBayesian statistical modelingandprobabilistic machine learning. It provides a robust framework for defining and fitting Bayesian networks to real data usingMarkov Chain Monte Carlo (MCMC)sampling techniques, as well asvariational inferencemethods.
This makes it a powerful tool for data scientists who want to work withuncertainty modeling,probabilistic inference, andcausal reasoningin complex datasets.
Let's clarify the other options to avoid confusion:
* Option A: SciLib- There is no standard Python library by this name that is related to Bayesian networks. (It may be confused withSciPyorSciKit-Learn, but those are not specialized for Bayesian inference.)
* Option C: MyLib- This is not a recognized Python package in the data science ecosystem.
* Option D: MCMC- While Markov Chain Monte Carlo is thetechniqueused in Bayesian estimation, it is not a standalone library. Instead, PyMC implements MCMC as part of its computational framework.
* Option E: SCIMC- No such Python library exists; it appears to be a distractor.
PyMC's primary strength is its ability to let data scientists define models in aprobabilistic programming style, making it easier to represent uncertainties and hidden variables in data. This aligns with DASCA's emphasis on ensuring data scientists understand bothstatistical foundationsand thetools required to implement them programmatically.
In practice, PyMC is often used in applications such as:
* Forecasting(e.g., time series with uncertainty bounds)
* Causal inference(estimating hidden relationships in data)
* Risk modeling(finance, healthcare, or supply chain domains)
* Machine learning with uncertainty quantification
Thus,PyMCis the correct library for fitting Bayesian networks in Python.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Programming for Data Science & Probabilistic Modeling Tools, Official DASCA Study Guide.

NEW QUESTION # 54
Which of the following is an example of graphical model?

• A. Bayesian Networks
• B. Both A and B
• C. Both A and C
• D. Geographical Networks
• E. Markov Random Fields

Answer: B

Explanation:
Graphical models are probabilistic models that represent variables and dependencies using graphs:
Markov Random Fields (Option A): Undirected graphical models that capture joint distributions over variables with neighborhood dependencies.
Bayesian Networks (Option B): Directed acyclic graphical models that encode conditional dependencies between random variables.
Geographical Networks (Option C): While they are graphs, they are not probabilistic graphical models used in statistics/ML.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graphical Models (Bayesian Networks
& Markov Random Fields).

NEW QUESTION # 55
Which of the following is a useful feature of functional programming?

• A. Higher-Order Functions (HOFs)
• B. Lazy Evaluation
• C. Immutable Data
• D. All of the above

Answer: D

Explanation:
Functional programming (FP) is a paradigm widely adopted in data science and big data tools (e.g., Spark with Scala/Python). Its useful features include:
Option A (Higher-Order Functions): Functions can take other functions as arguments or return them, enabling powerful abstractions like map(), reduce(), and filter().
Option B (Immutable Data): Ensures reliability and thread-safety, crucial for distributed computing. Once created, data structures cannot be modified, preventing side effects.
Option C (Lazy Evaluation): Computations are delayed until results are needed, improving performance in large-scale data operations.
Since FP leverages all three features, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Functional Programming in Data Science Tools.

NEW QUESTION # 56
Which of these are open-source column-oriented databases?

• A. Cassandra
• B. Both A and B
• C. HBase
• D. All of the above
• E. Accumulo

Answer: D

Explanation:
Column-oriented databases store data by columns rather than by rows, enabling efficient queries over large datasets, especially in analytical workloads.
Cassandra (Option A): An open-source, highly scalable, distributed column-oriented NoSQL database.
HBase (Option B): An open-source, Hadoop-based, column-family NoSQL database modeled after Google BigTable.
Accumulo (Option C): An open-source, secure, sorted, distributed key/value store built on top of HDFS and based on Google BigTable.
Since all three (A, B, and C) are open-source column-oriented databases, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Columnar Databases & NoSQL Ecosystem.

NEW QUESTION # 57
Which of the following architectural techniques is used for parallel processing?

• A. Both B and C
• B. The SuperVector Technique
• C. Both A and B
• D. Very Long Instruction Words (VLIW) Technique
• E. The Superscalar Technique

Answer: C

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 # 58
ARIMA model is:

• A. Autoregressive moving average
• B. Autoreactive moving average
• C. All of the above
• D. Autoresponsive moving average
• E. Autointeractive moving average

Answer: A

Explanation:
ARIMA stands for AutoRegressive Integrated Moving Average, one of the most widely used models for time series forecasting.
AutoRegressive (AR): Model uses past values of the variable to predict future values.
Integrated (I): Differencing is applied to make the time series stationary.
Moving Average (MA): Model incorporates past forecast errors into predictions.
Option B: Correct - autoregressive + moving average is part of ARIMA's name.
Options A, C, D: Incorrect because these terms are not recognized statistical modeling frameworks.
Option E: Incorrect, since only B is valid.
Thus, the correct answer is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Models (AR, MA, ARIMA).

NEW QUESTION # 59
What is TRUE for "rehashing"?

• A. Both A and B
• B. Key/value pairs from the original table can be inserted into the new, larger one
• C. It requires a new hash function, which maps values into a larger range of integers
• D. All of the above
• E. Allocate a new, larger hash table in memory

Answer: D

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 # 60
Example of amortized performance is:

• A. MapReduce dictionaries
• B. HDFS dictionaries
• C. Python dictionaries
• D. All of the above
• E. Hadoop dictionaries

Answer: C

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 # 61
IoT is built on:

• A. Both A and B
• B. None of the above
• C. Networks of data gathering devices
• D. Cloud Computing

Answer: A

Explanation:
The Internet of Things (IoT) is an ecosystem of interconnected devices that collect, transmit, and analyze data. IoT relies on two critical foundations:
Option A (Cloud Computing): IoT generates massive amounts of data, and cloud platforms provide scalable storage, analytics, and computing resources for real-time and batch processing.
Option B (Networks of data gathering devices): IoT relies on physical devices - sensors, smart appliances, industrial machines - that collect and transmit data through networks (Wi-Fi, Bluetooth, 5G, LPWAN).
Thus, IoT is fundamentally built on both cloud computing and networks of devices, making Option C correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data & IoT Ecosystem Fundamentals.

NEW QUESTION # 62
Self-driving car is an example of:

• A. Reinforcement learning
• B. Supervised learning
• C. Unsupervised learning
• D. All of the above

Answer: A

Explanation:
Self-driving cars (autonomous vehicles) are an application of Reinforcement Learning (RL) in machine learning:
In RL, an agent (car) interacts with an environment (roads, obstacles, traffic) and learns to maximize rewards (e.g., safe driving, efficient navigation).
The system improves performance through trial-and-error learning, guided by reward signals such as staying in a lane or avoiding collisions.
Supervised learning (A): Used in some supporting tasks like image recognition (e.g., identifying stop signs), but not the core paradigm for self-driving.
Unsupervised learning (B): Useful for clustering sensor data, but again not the main paradigm.
Reinforcement learning (C): Correct, since self-driving fundamentally depends on RL decision-making.
Thus, the correct answer is Option C (Reinforcement Learning).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Machine Learning Paradigms: Reinforcement Learning and Autonomous Systems.

NEW QUESTION # 63
A workflow refers to a:

• A. Indirected cyclic graph
• B. Directed cyclic graph
• C. Indirected acyclic graph
• D. Directed acyclic graph

Answer: D

Explanation:
In data pipelines and process orchestration, a workflow is represented as a Directed Acyclic Graph (DAG):
Directed: Each edge has a direction, representing task dependencies.
Acyclic: No cycles exist; tasks must follow a sequence without looping back.
Graph: Represents tasks as nodes and dependencies as edges.
This structure is common in tools like Apache Airflow, Spark DAGs, and Hadoop MapReduce job schedulers.
Option A & B: Incorrect, as workflows cannot have cycles (would cause infinite loops).
Option D: Incorrect, because workflows are directed, not indirected.
Thus, the correct answer is Option C (Directed Acyclic Graph).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Workflow Management with DAGs.

NEW QUESTION # 64
Which of the following statements is correct?

• A. 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
• B. 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
• C. 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
• D. 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

Answer: D

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 # 65
Which of the following is TRUE about monetization exercise?

• A. Both B and C
• B. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
• C. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
• D. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
• E. All of the above

Answer: E

Explanation:
The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.
Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.
Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.
Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).
Thus, all three statements are valid, making the correct answer Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Data Monetization Strategies.

NEW QUESTION # 66
Which of the following phases is NOT a Big Data Business Model Maturity Index?

• A. Business Strategy
• B. Business Monitoring
• C. Business Metamorphosis
• D. Business Optimization
• E. Data Monetization

Answer: A

Explanation:
The Big Data Business Model Maturity Index (BDBMMI) defines phases organizations pass through in leveraging data strategically:
Business Monitoring (A): Tracking metrics and reporting.
Business Insights (not listed in options but part of the framework).
Business Optimization (B): Using analytics to improve efficiency.
Data Monetization (D): Creating new revenue streams with data.
Business Metamorphosis (E): Transforming the business model through data.
Business Strategy (Option C): While strategy is essential, it is not one of the defined phases of BDBMMI.
Thus, the correct answer is Option C (Business Strategy).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Business Model Maturity Index (BDBMMI).

NEW QUESTION # 67
A burn down chart shows:

• A. The rate of reduction of budget for a project
• B. The volume of work and features completed
• C. The declining energy of the team
• D. The number of hours worked after dark

Answer: B

Explanation:
A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.
Option A: Incorrect. Burn down charts don't measure team "energy" or motivation levels.
Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.
Option C: Incorrect. Hours worked after dark is irrelevant.
Option D: Incorrect. Budget reduction is not tracked in burn down charts.
Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Project Management & Agile Tools in Data Science.

NEW QUESTION # 68
Which of the following is NOT a valid section of Big Data Strategy document?

• A. Business strategy
• B. Key business initiatives
• C. Key decisions
• D. Business decisions
• E. Key business entities

Answer: D

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 # 69
Which of the following is correct?

• A. DataFrame is similar to SQL tables or R data frames
• B. Both A and B
• C. The central object in Pandas is called a DataFrame
• D. A data frame is a table with rows and columns
• E. All of the above

Answer: E

Explanation:
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).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Pandas Data Structures.

NEW QUESTION # 70
Business Intelligence (BI) is:

• A. BI focuses on reporting on the future state of the business
• B. Both B and C
• C. BI focuses on descriptive analytics
• D. Both A and B
• E. BI focuses on "What happened?"

Answer: D

Explanation:
Business Intelligence (BI) is primarily focused on descriptive analytics and reporting - understanding historical and current business performance.
Option A (Descriptive analytics): Correct. BI uses dashboards, reports, and OLAP tools to summarize what has occurred in the past.
Option B ("What happened?"): Correct. BI answers retrospective questions by analyzing transactional and operational data.
Option C (Future state): Incorrect. Predicting future business outcomes falls under predictive analytics or advanced analytics, not BI.
Thus, the correct answer is Option D (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization & BI: Descriptive Analytics and Reporting.

NEW QUESTION # 71
......

Ultimate Guide to Prepare SDS Certification Exam for DASCA Data Scientist: https://pass4sure.dumptorrent.com/SDS-braindumps-torrent.html