DASCA Data Scientist Certification SDS Sample Questions Reliable
Prepare for the Actual DASCA Data Scientist SDS Exam Practice Materials Collection
NEW QUESTION # 48
Which of the following is a "thinking like a data scientist" decomposition process?
- A. Business Stakeholder
- B. All of the above
- C. Business Initiative
- D. Strategic Nouns
- E. Both B and C
Answer: B
Explanation:
The "Thinking Like a Data Scientist" (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:
Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).
Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.
Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.
Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Science Fundamentals: Thinking Like a Data Scientist Process.
NEW QUESTION # 49
Which of the following is an example of NLP?
- A. Finding which text documents are about similar topics
- B. Using Twitter to assess public sentiment
- C. All of the above
- D. Both A and C
- E. Flagging e-mails as spam
Answer: C
Explanation:
Natural Language Processing (NLP) is the field of machine learning that deals with human language understanding and generation. Applications include:
Option A (Spam detection): NLP techniques classify emails based on text patterns and context.
Option B (Sentiment analysis on Twitter): NLP models analyze textual data to extract emotions, opinions, and trends.
Option C (Topic modeling): NLP clustering and probabilistic models (e.g., LDA) classify documents by semantic similarity.
Since all are valid NLP applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: NLP in Real- World Use Cases.
NEW QUESTION # 50
Which of the following is True about Time Series Analysis?
- A. Predicting when/whether an event will occur, such as a failure of the machine generating the data
- B. All of the above
- C. Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
- D. Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
- E. Both A and B
Answer: B
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 # 51
Which of the following is TRUE for Tensor?
- A. In Tensor, there can be arbitrarily many dimensions to the array
- B. Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
- C. Tensor is an array of floating-point numbers
- D. All of the above
- E. Both B and C
Answer: D
Explanation:
A Tensor is a fundamental data structure in modern machine learning frameworks (e.g., TensorFlow, PyTorch). It is best described as a generalization of vectors and matrices to potentially higher dimensions.
Option A: Correct. Tensors typically store numeric values (commonly floating-point numbers) in structured formats.
Option B: Correct. A tensor can have any number of dimensions (rank). For example:
A scalar is a 0-D tensor.
A vector is a 1-D tensor.
A matrix is a 2-D tensor.
Higher-rank tensors can represent images, videos, or multidimensional datasets.
Option C: Correct. Tensors are explicitly designed to allow linear algebra operations, which are the foundation of deep learning computations (matrix multiplications, dot products, etc.).
Therefore, since all three statements are true, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics and Machine Learning, Deep Learning Concepts; Official DASCA Study Guide.
NEW QUESTION # 52
Which classification steps are performed in inductive techniques?
i. Training Step
ii. Test Step
iii. Validation Step
iv. Application Step
- A. i, ii
- B. i, ii, iii, iv
- C. ii, iii
- D. i, ii, iv
Answer: B
Explanation:
Inductive learning techniques in machine learning (such as decision trees, neural networks, or SVMs) follow a systematic sequence of steps for classification:
Training Step (i): A model is built using training data, where the system learns relationships between features and target labels.
Test Step (ii): The trained model is evaluated on unseen test data to measure its performance and generalizability.
Validation Step (iii): Often, a validation set is used to fine-tune model parameters, avoid overfitting, and choose the best model configuration.
Application Step (iv): The final validated model is applied to classify new, real-world data.
Since all four steps (i, ii, iii, iv) are essential to inductive classification, the correct answer is Option D (i, ii, iii, iv).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics & Machine Learning: Classification and Inductive Learning Techniques.
NEW QUESTION # 53
Which of the following is the common evolutionary algorithm of neural networks?
- A. All of the above
- B. Genetic programming
- C. Genetic algorithm
- D. Evolution strategy
Answer: A
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 # 54
What is TRUE for "rehashing"?
- A. Key/value pairs from the original table can be inserted into the new, larger one
- B. It requires a new hash function, which maps values into a larger range of integers
- C. All of the above
- D. Allocate a new, larger hash table in memory
- E. Both A and B
Answer: C
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 # 55
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 can make both of the Business Intelligence and Data Science environments more agile and more productive
- C. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
- D. None of the above
- E. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
Answer: B,C,E
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 # 56
The Big Data Vision Workshop process is ideal for organizations who:
- A. All of the above
- B. Have a wealth of data that they do not know how to monetize
- C. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- D. Both A and B
- E. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
Answer: A
Explanation:
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.
NEW QUESTION # 57
Spark is written in:
- A. C
- B. C++
- C. Python
- D. Scala
- E. Java
Answer: D
Explanation:
Apache Spark is an open-source distributed computing framework widely used for big data processing and machine learning pipelines.
The core implementation of Spark is written in Scala (Option A), which runs on the JVM (Java Virtual Machine).
Spark also provides APIs for Java, Python (PySpark), R, and SQL, but its native language is Scala.
Options C (C) and D (C++) are incorrect; Spark is not written in these languages.
Python (Option E) is a supported API, but Spark itself is not written in Python.
Thus, the correct answer is Scala (Option A).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools for Big Data & Distributed Computing.
NEW QUESTION # 58
Semi-structured data does NOT include:
- A. Scientific data
- B. Schema-full data
- C. Database system
- D. File systems
Answer: B
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 # 59
What is Scrumban?
- A. It combines the principles of Scrum and Kanban into a push-based system
- B. It combines the principles of Scrum and Kanban into a pull-based system
- C. It is Kanban
- D. It is Scrum
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 # 60
Which of the following is used to summarize a dataset by showing the median, quantiles, and min/max values for each of the variables?
- A. Pie Charts
- B. Scatter Chart
- C. Histogram
- D. Bar Charts
- E. Box Plots
Answer: E
Explanation:
A Box Plot (also called Whisker Plot) is a visualization tool used to summarize data distribution using five- number summary:
Minimum,
First quartile (Q1),
Median (Q2),
Third quartile (Q3),
Maximum.
It also highlights outliers explicitly.
Option A (Box Plots): Correct.
Option B (Pie Charts): Show proportions, not distribution.
Option C (Histogram): Shows frequency distribution but not quartiles/median.
Option D (Scatter Chart): Used for relationships between two variables, not summary statistics.
Option E (Bar Charts): Compare categories, not statistical spread.
Thus, the correct answer is Option A (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots and Statistical Summaries.
NEW QUESTION # 61
Which of the following errors refers to the wrong negation of a true null hypothesis?
- A. Hypothesis Error
- B. Type II Error
- C. Type I Error
- D. Logical Error
- E. None of the above
Answer: C
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 # 62
Which of the following is TRUE about Avro?
- A. Both A and B
- B. Avro is based on Remote Procedure Call (RPC)
- C. None of the above
- D. Avro is a data serialization framework
Answer: A
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 # 63
Which of the following statements is correct?
- A. 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
- B. 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
- C. 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
- D. 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
Answer: B
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 # 64
Which of the following is NOT a valid section of Big Data Strategy document?
- A. Key business initiatives
- B. Key business entities
- C. Key decisions
- D. Business decisions
- E. Business strategy
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 # 65
The spokes of the "Hub and Spoke" analytics architecture are the analytic use cases or applications that help the organization to optimize:
- A. Uncover new monetization opportunities
- B. All of the above
- C. Key business processes
- D. Deliver a more compelling customer experience
- E. Both A and B
Answer: B
Explanation:
In the Hub and Spoke analytics architecture:
The hub is the central data platform (data lake, warehouse, or unified data hub).
The spokes are the analytic use cases or applications that leverage this data to create business value.
These spokes typically help the organization:
Optimize key business processes (Option A).
Deliver improved customer experiences (Option B).
Uncover monetization opportunities (Option C).
Since all three are valid, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Hub-and-Spoke Analytics.
NEW QUESTION # 66
Which of the following phases is NOT a Big Data Business Model Maturity Index?
- A. Business Optimization
- B. Business Strategy
- C. Business Metamorphosis
- D. Data Monetization
- E. Business Monitoring
Answer: B
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
IoT is built on:
- A. Both A and B
- B. Networks of data gathering devices
- C. None of the above
- 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 # 68
What is the agenda of discussion at a "stand up" meeting of an Agile team?
- A. What they accomplished the previous day
- B. What they are planning to do today
- C. All of the above
- D. Any roadblocks they are running into
- E. Both A and B
Answer: C
Explanation:
A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:
What was accomplished yesterday? (Progress review).
What is planned for today? (Work alignment).
What impediments or roadblocks exist? (Barriers identification).
This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.
Option A: Correct - yesterday's work is discussed.
Option B: Correct - today's planned tasks are outlined.
Option C: Correct - roadblocks are highlighted.
Option D: Incomplete since it misses C.
Option E: Correct - covers all agenda items.
Thus, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Practices in Data Science Projects.
NEW QUESTION # 69
The main purpose of a Statement Of Work (SOW) is to get:
- A. What expectations are realistic
- B. All of the above
- C. Everybody on the same page about what work should be done
- D. What the priorities are
- E. None of the above
Answer: B
Explanation:
A Statement of Work (SOW) is a formal document that defines the scope, objectives, deliverables, timeline, and expectations of a project. In data science and IT projects, it ensures:
Clarity of scope (Option A): Everyone understands exactly what work should be done.
Clear priorities (Option B): It defines what is most critical for success.
Realistic expectations (Option C): It aligns stakeholders by setting measurable and achievable goals.
Since all of these are essential purposes of an SOW, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications: Project Governance and SOW.
NEW QUESTION # 70
Which of the following is NOT a correct situation to use Agile?
- A. None of the above
- B. When changes need to be implemented during the entire process
- C. When the final product isn't clearly defined
- D. When clients/stakeholders need to be able to change the scope
Answer: A
Explanation:
Agile methodology is widely adopted in data science projects because these projects often involve uncertain goals, exploratory analysis, and changing requirements. Agile thrives in environments where iteration, collaboration, and adaptability are necessary.
Option A: True for Agile. If the final product is unclear (common in data science), Agile works well because it allows incremental discovery and iterative prototyping.
Option B: True for Agile. Agile frameworks (Scrum, Kanban) emphasize flexibility, which means the scope can evolve as stakeholders learn more from data and models.
Option C: True for Agile. Agile welcomes continuous changes through iterative sprints and feedback loops.
This adaptability is crucial in machine learning model development where data insights often reshape project direction.
Since all three situations are valid for Agile, the correct answer to "Which is NOT correct?" is None of the above (Option D).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science & Agile Methodologies in Data Projects.
NEW QUESTION # 71
SpamAssassin has been developed to detect:
- A. Spam emails
- B. None of the above
- C. Email with big attachments
- D. Email with virus
Answer: A
Explanation:
Apache SpamAssassin is one of the most widely used open-source tools for spam email detection.
It applies a rule-based system combined with Bayesian filtering, heuristics, and collaborative filtering methods to classify incoming emails as spam or legitimate.
Option A (Spam emails): Correct, this is the main function.
Option B (Big attachments): Incorrect. Large attachment filtering is not its primary purpose.
Option C (Email with virus): Incorrect. That falls under antivirus or malware detection tools, not SpamAssassin.
Option D: Incorrect since A is valid.
Thus, the correct answer is Option A (Spam emails).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Email Filtering and Text Mining.
NEW QUESTION # 72
Which of the following is the most important part of Hadoop?
- A. Spark Framework
- B. Hadoop Distributed File System (HDFS)
- C. Both A and B
- D. MapReduce Framework
- E. Both B and C
Answer: C
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 # 73
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Ace DASCA SDS Certification with Actual Questions Feb 07, 2026 Updated: https://www.torrentvce.com/SDS-valid-vce-collection.html
DASCA Data Scientist Certified Official Practice Test SDS: https://drive.google.com/open?id=1QumJmSE3IqeLbJdS7HO4Dj_IlQWH_c3K