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NEW QUESTION # 22
Exploratory analytic algorithms help the Data Science team to better:
- A. Understand the data content
- B. Gain a high-level understanding of relationships
- C. All of the above
- D. Understand patterns in the data
- E. Both A and B
Answer: C
Explanation:
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).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning: Exploratory Analytics & EDA.
NEW QUESTION # 23
Example of amortized performance is:
- A. HDFS dictionaries
- B. Hadoop dictionaries
- C. All of the above
- D. Python dictionaries
- E. MapReduce dictionaries
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 # 24
Which of the following is TRUE about Avro?
- A. Avro is a data serialization framework
- B. Both A and B
- C. None of the above
- D. Avro is based on Remote Procedure Call (RPC)
Answer: B
Explanation:
Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.
Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.
Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.
Option C: Correct, since both statements are true.
Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.
In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem Tools & Data Serialization Techniques.
NEW QUESTION # 25
A burn down chart shows:
- A. The declining energy of the team
- B. The volume of work and features completed
- C. The number of hours worked after dark
- D. The rate of reduction of budget for a project
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 # 26
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. Trend analysis
- B. High Curve Equivalent (HCE)
- C. Normal Curve Equivalent (NCE)
- D. Medium Curve Equivalent (MCE)
- E. None of the above
Answer: C
Explanation:
Normal Curve Equivalent (NCE) scores are standardized scores designed to:
Range between 1 and 99.
Be comparable to percentile ranks but with the advantage of equal-interval properties like Z-scores.
This makes NCE scores useful in educational assessments, survey analysis, and statistical modeling.
Option A (Trend analysis): Incorrect. Not related to score standardization.
Option B (Correct): NCE fits the definition perfectly.
Option C (HCE) & D (MCE): Not recognized standard measures in statistics.
Option E: Incorrect, since Option B is valid.
Thus, the correct answer is Option B: Normal Curve Equivalent (NCE).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Methods in Data Science: Z-scores, Percentiles, and NCE.
NEW QUESTION # 27
IoT is built on:
- A. Networks of data gathering devices
- B. Cloud Computing
- C. Both A and B
- D. None of the above
Answer: C
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 # 28
Which of the following is the most important part of Hadoop?
- A. Spark Framework
- B. Hadoop Distributed File System (HDFS)
- C. MapReduce Framework
- D. Both A and B
- E. Both B and C
Answer: D
Explanation:
The Hadoop ecosystem consists of multiple components, but the two core components that define Hadoop are:
HDFS (Hadoop Distributed File System): Provides fault-tolerant, scalable storage across distributed clusters.
It is the backbone for storing massive datasets in a distributed fashion.
MapReduce Framework: Provides the parallel computing and data processing layer in Hadoop, enabling batch analysis over distributed datasets.
Option A: Correct, HDFS is essential.
Option B: Correct, MapReduce is essential.
Option C: Incorrect, Spark is a newer processing framework, but it is not originally part of Hadoop core.
Option D: Correct answer since both HDFS and MapReduce are considered the fundamental parts of Hadoop.
Option E: Incorrect, because Spark is not a core Hadoop component (though it integrates with Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystems: Hadoop Architecture & Components.
NEW QUESTION # 29
What is TRUE for "rehashing"?
- A. Allocate a new, larger hash table in memory
- B. It requires a new hash function, which maps values into a larger range of integers
- C. Key/value pairs from the original table can be inserted into the new, larger one
- D. All of the above
- E. Both A and B
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 # 30
What is DevOps?
- A. Software Development
- B. All
- C. Quality Assurance
- D. Software Operations
Answer: B
Explanation:
DevOps is not just about coding (development) or system administration (operations). It is a holistic cultural and technical practice that unifies:
Software Development (Option A): Writing and building applications.
Software Operations (Option B): Deploying, monitoring, and maintaining systems in production.
Quality Assurance (Option C): Ensuring the reliability, security, and performance of applications through testing and automation.
Thus, DevOps encompasses all three dimensions, making the correct answer Option D (All).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: DevOps & Continuous Delivery.
NEW QUESTION # 31
Which of the following can be classified as factor analysis in machine learning?
- A. Confirmatory factor analysis
- B. Exploratory factor analysis
- C. Both A and B
- D. None of the above
Answer: C
Explanation:
Factor analysis is a dimensionality reduction technique used to uncover latent variables (factors) that explain observed patterns of correlations in data. It is widely used in psychometrics, social sciences, and machine learning.
Exploratory Factor Analysis (EFA, Option A): Used when the underlying factor structure is unknown, aiming to discover potential latent variables.
Confirmatory Factor Analysis (CFA, Option B): Used when there is a hypothesis about factor structure, and the goal is to confirm it statistically.
Both are valid approaches to factor analysis, hence the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Dimensionality Reduction & Factor Analysis in Machine Learning.
NEW QUESTION # 32
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 # 33
Which of the following is TRUE for data lake?
- A. The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
- B. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
- C. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
- D. The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive
- E. None of the above
Answer: B,C,D
Explanation:
But per MCQ single-choice format # answer: A (though ideally A, B, C are correct).
A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:
Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.
Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.
Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.
Option D: Incorrect. Data lakes improve agility, not reduce it.
Option E: Incorrect, since multiple true statements exist.
Thus, the correct choice per DASCA context is Option A (with B and C also being true).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering: Data Lakes vs Warehouses.
NEW QUESTION # 34
Which of the following errors refers to the wrong negation of a true null hypothesis?
- A. Type II Error
- B. None of the above
- C. Logical Error
- D. Type I Error
- E. Hypothesis Error
Answer: D
Explanation:
In hypothesis testing, two main types of errors are defined:
Type I Error (Option A): Occurs when the null hypothesis (H#) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.
Type II Error (Option B): Occurs when the null hypothesis (H#) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.
Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.
Thus, the "wrong negation of a true null hypothesis" refers to a Type I Error (false positive).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations in Data Science: Hypothesis Testing & Errors.
NEW QUESTION # 35
Which of the following is FALSE for Social Network Analysis (SNA)?
- A. Social Network Analysis (SNA) is an example of graph analysis
- B. Social Network Analysis (SNA) is an example of trend analysis
- C. SNA is used to investigate social structures and relationships across social networks
- D. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
- E. None of the above
Answer: B
Explanation:
Social Network Analysis (SNA) is a powerful analytical method that applies graph theory to study relationships among entities (people, organizations, computers, etc.).
Option A: Correct. SNA is indeed an example of graph analysis because it models entities as nodes and their relationships as edges/ties.
Option B: FALSE. SNA is not an example of trend analysis. Trend analysis focuses on temporal patterns (time series), while SNA is structural and relational.
Option C: Correct. SNA investigates structures such as communities, influencers, and information diffusion in networks.
Option D: Correct. The characterization of nodes and edges is central to SNA.
Option E: Incorrect, since we've identified Option B as false.
Thus, the false statement is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graph Analysis & Social Network Analysis.
NEW QUESTION # 36
Spark programs can be written in:
- A. Scala
- B. Python
- C. All of the above
- D. None of the above
- E. Java
Answer: C
Explanation:
Apache Spark supports multiple programming languages for developing distributed applications:
Java (Option A): Supported through Spark's JVM-based APIs.
Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.
Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.
Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.
Thus, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools: Spark APIs for Java, Scala, Python, and R.
NEW QUESTION # 37
Which of the following is a trend analysis component of time series decomposition?
- A. Cyclical
- B. Seasonal
- C. All of the above
- D. Irregular
- E. Both A and B
Answer: C
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 # 38
Semi-structured data does NOT include:
- A. File systems
- B. Scientific data
- C. Database system
- D. Schema-full data
Answer: D
Explanation:
Semi-structured data falls between structured data (e.g., relational databases with fixed schema) and unstructured data (e.g., free text, audio, video). It typically includes irregular or flexible schema information, such as XML, JSON, email data, or log files.
Option A (Database systems): Correct, databases may hold semi-structured content (e.g., JSON or XML columns).
Option B (File systems): Correct, file-based storage (logs, JSON, Avro, CSV) often contains semi-structured data.
Option C (Scientific data): Correct, many scientific applications generate semi-structured data formats (sensor readings, genomic sequences, etc.).
Option D (Schema-full data): Correct Answer. Schema-full (strict schema-defined relational tables) represent structured data, not semi-structured.
Thus, semi-structured data does NOT include schema-full data.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Data Types & Sources.
NEW QUESTION # 39
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