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DataOps Methodology Cognitive Class Exam Quiz Answers

DataOps Methodology Cognitive Class Certification Answers

Question 1: Before we can put together a data strategy, we need to have a good understanding of the data available and how it is used in the organization.

  • True
  • False

Question 2: What is a data strategy?

  • An architecture and actionable roadmap along with an action plan
  • A competitive publication to show that our organization is modern
  • A plan to move all legacy data systems to the cloud

Question 3: Implementing a data strategy should always result in cost savings in the year the plan is realized.

  • True
  • False

Question 4: Which of the following statements about Data Strategy are correct?

  • Whatever the type of data, it should only include internally produced data
  • All types of data – both structured and unstructured need to be considered
  • Volumes of data have increased hugely, but are now starting to stabilize
  • Only business executives should be consulted in putting together a strategy

Question 5: Data Governance is a key part of executing a data strategy.

  • True
  • False

Question 1: A DataOps team consists of members mostly from IT departments.

  • True
  • False

Question 2: Which of the following roles are active team members of any DataOps team?

  • Chief Technology Officer
  • Chief Data Officer
  • Data Engineer
  • Database Administrator
  • Data Steward
  • Data Architect
  • Data Scientist

Question 3: Creating and maintain business terms is a major responsibility of which following role?

  • Data Engineer
  • Data Quality Analyst
  • Data Steward
  • Data Scientist

Question 4: Only Chief Data Officer can update the KPIs for a data sprint.

  • True
  • False

Question 5: DataOps relies heavily on the use of automation, so that communication among team members is not necessary.

  • True
  • False

Question 1: DataOpstoolchain helps you deliver quality data slowly.

  • True
  • False

Question 2: DataOpsToolchain and DevOps are the same thing.

  • True
  • False

Question 3: DataOpsToolchain can work without DataOps API(s).

  • True
  • False

Question 4: What are the key components of DataOpsToolchain?

  • Continuous Deployment
  • Communication
  • Source Control
  • All of above

Question 5: Who is responsible for creating DataOpsToolchain? (Choose all that apply)

  • Data Scientist
  • Administrator
  • DBA
  • Data Engineer

Question 1: Data Management is the same as Information Governance.

  • True
  • False

Question 2: What is the most costly result from an external influence to an organization?

  • Data Breach Fines and Penalties
  • Insurance Policy Payout
  • Claim Settlement
  • None of these

Question 3: Reference data is defined as data used as a permissible value within a data field.

  • True
  • False

Question 1: Business Priority should be the primary focus when deciding what the DataOps team should do.

  • True
  • False

Question 2: What is a data backlog?

  • A bottleneck in the data pipeline
  • A list of all data sources
  • A prioritized set of requirements expressed as data tasks
  • A plan to move all data into a catalog

Question 3: A prioritized data backlog will reduce the time taken to start the next DataOps iteration.

  • True
  • False

Question 4: A Data Task should be prioritized by considering:

  • The cost of providing the data
  • The career advancement possibilities of solving business challenges
  • The impact to sales from implementing the data pipeline
  • All of the above

Question 5: KPIs are used to determine the progress and throughput of a DataOps data sprint.

  • True
  • False

Question 1: You will need someone on your team with detailed knowledge of the business processes you’re going to analyze so selected data elements are appropriate to reaching your objectives.

  • True
  • False

Question 2: What should you do if you identify gaps or mismatches in the data required for the analysis?

  • Rethink how you will do the analysis with different data
  • Create the missing data
  • Find a new source for the missing or mismatched data
  • All of the above

Question 3: You should trace the linage of data elements to be used for analysis to make sure they come from a trusted source.

  • True
  • False

Question 4: What is the primary objective of the Discover phase?

  • Decide what the analytics team wants to have for lunch
  • Identify and locate the specific data elements required to accomplish an analysis
  • Uncover the meaning of data column headers and how they relate to the underlying data
  • Gain an understanding of the business goals and KPIs of an analysis effort

Question 5: A Data Engineer who thoroughly understands where specific data resides, including the specific databases and files where each identified data element resides, should be involved in Data Discovery process.

  • True
  • False

Question 1: Classification of each data element will make it easier going forward for users to distinguish the meaning and applicability of the data for their purposes.

  • True
  • False

Question 2: Which description best defines taxonomy?

  • Organizing data elements into meaningful structures
  • An IBM network protocol which reduces network latency
  • The art of preparing, stuffing, and mounting the skins of animals with lifelike effect

Question 3: A single data element can be placed into an unlimited number of data domains.

  • True
  • False

Question 4: Which of the following is the objective of classification?

  • To bring out points of similarity and dissimilarity among various groups
  • To present data in a simple, logical and understandable form
  • To condense the mass of data
  • All of the above

Question 5: You should design workflows which are specific to the classification tool you are using.

  • True
  • False

Question 1: Data quality is data accuracy.

  • True
  • False

Question 2: All data across the enterprise should have the same data quality.

  • True
  • False

Question 3: A data quality framework consists of which of the following 4 phases:

  • Profile
  • Define
  • Remediate
  • Monitor
  • Assess
  • Deploy

Question 4: When assessing data quality, you only need the data set containing the data, metadata is optional.

  • True
  • False

Question 1: How does data classification affect defining policies?

  • Inheritance, retention and probabilities
  • Protection, reporting and inheritance
  • Protection, accessibility and retention
  • Retention, deletion and storage

Question 2: What impact does a highly sensitive classification have on a policy definition?

  • Require data anonymization, de-identification, and masking
  • Limit access to the data and/or require data masking
  • Limit access to the data and make it unprintable
  • No impact

Question 3: What are the most common state, country or regional regulations affecting personal information?

  • SIN, SSN and BAN
  • FDIC, BCBS and SOX
  • CCPA, GDPR and LGPD
  • PCI, PII and PHI

Question 4: Once policies have been defined affecting the data, rules must be enforced to act.

  • True
  • False

Question 1: Self Service of data is only possible when any data movement and transformation required to join multiple data assets have been performed.

  • True
  • False

Question 2: Self Service can use the following governance artefacts to refine a search in a catalog. (Choose all that apply)

  • Data Protection Rules
  • Business Terms
  • Tags

Question 3: A data consumer should not be able to access data that has been identified as sensitive, where there is not a business need to do so.

  • True
  • False

Question 4: Which of the following statements about Self Service are correct?

  • Data consumers typically do not know how to manipulate the data
  • Data Protection rules prevent a data consumer from inadvertently seeing data that is sensitive
  • Creating multiple catalogs can partition data assets by their content and anticipated audience
  • A data consumer needs to know SQL to join multiple data assets

Question 5: Data Consumers provide valuable input to data scientists by clarifying the combination of data assets and how they need to be transformed, prior to data movement being designed and implemented.

  • True
  • False

Question 1: You should define the use case at the outset of a Data Movement and Integration project to support a “Build It and They Will Come” strategy.

  • True
  • False

Question 2: Which of the following does not represent a data integration pattern:

  • Data virtualization
  • Data replication
  • Data lineage
  • Message-oriented movement
  • Bulk/batch

Question 3: Which of the following is not a Data Movement and Integration Job Design consideration?

  • Design for reusability
  • Deployment models (e.g. Containers, Kubernetes Orchestration, OpenShift)
  • Design for parallel processing
  • Everything should be programmed in Python
  • Design for job portability (build once and run anywhere)

Question 4: Hand coding generally provides a 10X productivity gain over commercial data integration software tooling.

  • True
  • False

Question 5: Which of the following is not an example of a message queuing system?

  • Kafka
  • VSAM
  • Microsoft Azure Queues
  • GCP PubSub
  • AWS Simple Queue Service
  • MQ

Question 1: DataOps is a completely new methodology and it doesn’t learn anything from agile and devOps.

  • True
  • False

Question 2: Data consumers can first start to provide feedback to the current data sprint in the stakeholder review meeting.

  • True
  • False

Question 3: Which of the following assets or artifacts could be found in catalog?

  • Code
  • Business terms
  • Data rules
  • Source data
  • Data lineage

Question 4: All issues need to be remediated before moving on to the next data sprint.

  • True
  • False

Question 5: Completing a data sprint involves publishing governed artifacts and data assets to a production environment.

  • True
  • False

Question 1: DataOps is a fixed process which should not be changed once defined.

  • True
  • False

Question 2: Improvements to the DataOps process could involve changes to

  • Technology used in DataOps
  • DataOps team roles and responsibilities
  • Processes for ETL
  • All of the above

Question 3: Reviewing the Data classification phase involves reviewing how accurate the data mappings to the business terms are.

  • True
  • False

Question 4: Reviewing the Establish Baseline Process should include reviewing how effective the processes are for establishing a baseline for –

  • External Regulatory requirements
  • Organization maturity and Readiness
  • Governance and Oversight
  • All of the above

Question 5: KPIs are key in determining the effectiveness of all parts of the DataOps process.

  • True
  • False

Question 1: What is a data strategy?

  • An architecture and actionable roadmap along with an action plan
  • A competitive publication to show that our organization is modern
  • A plan to move all legacy data systems to the cloud

Question 2: Which of the following statements about Data Strategy are correct?

  • Whatever the type of data, it should only include internally produced data
  • All types of data – both structured and unstructured need to be considered
  • Volumes of data have increased hugely, but are now starting to stabilize
  • Only business executives should be consulted in putting together a strategy

Question 3: Which of the following roles are active team members of any DataOps team?

  • Chief Technology Officer
  • Chief Data Officer
  • Data Engineer
  • Database Administrator
  • Data Steward
  • Data Architect
  • Data Scientist

Question 4: Creating and maintaining business terms is a major responsibility of which following role?

  • Data Engineer
  • Data Quality Analyst
  • Data Steward
  • Data Scientist

Question 5: Business Priority should be the primary focus when deciding what the DataOps team should do.

  • True
  • False

Question 6: What is a data backlog?

  • A bottleneck in the data pipeline
  • A list of all data sources
  • A prioritized set of requirements expressed as data tasks
  • A plan to move all data into a catalog

Question 7: A Data Task should be prioritized by considering:

  • The cost of providing the data
  • The career advancement possibilities of solving business challenges
  • The impact to sales from implementing the data pipeline
  • All of the above

Question 8: KPIs are used to determine the progress and throughput of a DataOps data sprint.

  • True
  • False

Question 9: What are key components of DataOps toolchain?

  • Continuous Deployment
  • Communication
  • Source Control
  • All of above

Question 10: Who is responsible for creating DataOps toolchain? (Choose all that apply)

  • Data Scientist
  • Administrator
  • DBA
  • Data Engineer

Question 11: What is the primary objective of the Discover phase?

  • Decide what the analytics team wants to have for lunch.
  • Identify and locate the specific data elements required to accomplish an analysis
  • Uncover the meaning of data column headers and how they relate to the underlying data.
  • Gain an understanding of the business goals and KPIs of an analysis effort.

Question 12: Which description best defines taxonomy?

  • Organizing data elements into meaningful structures.
  • An IBM network protocol which reduces network latency.
  • The art of preparing, stuffing, and mounting the skins of animals with lifelike effect.

Question 13: Which of the following is the objective of classification?

  • To bring out points of similarity and dissimilarity among various groups.
  • To present data in a simple, logical and understandable form.
  • To condense the mass of data.
  • All of the above

Question 14: A data quality framework consists of which of the following 4 phases:

  • Profile
  • Define
  • Remediate
  • Monitor
  • Assess
  • Deploy

Question 15: How does data classification affect defining policies?

  • Inheritance, retention and probabilities
  • Protection, reporting and inheritance
  • Protection, accessibility and retention
  • Retention, deletion and storage

Question 16: What impact does a highly sensitive classification have on a policy definition?

  • Require data anonymization, de-identification, and masking
  • Limit access to the data and/or require data masking
  • Limit access to the data and make it unprintable
  • No impact

Question 17: Self Service can use the following governance artefacts to refine a search in a catalog. (Choose all that apply)

  • Data Protection Rules
  • Business Terms
  • Tags

Question 18: Which of the following statements about Self Service are correct?

  • A data consumer needs to know SQL to join multiple data assets
  • Data Protection rules prevent a data consumer from inadvertently seeing data that is sensitive
  • Creating multiple catalogs can partition data assets by their content and anticipated audience
  • Data consumers typically do not know how to manipulate the data

Question 19: Which of the following does not represent a data integration pattern:

  • Data virtualization
  • Data replication
  • Data lineage
  • Message-oriented movement
  • Bulk/batch

Question 20: Which of the following is not a Data Movement and Integration Job Design consideration?

  • Design for reusability
  • Deployment models (e.g. containers, Kubernetes orchestration, OpenShift)
  • Design for parallel processing
  • Everything should be programmed in Python
  • Design for job portability (build once and run anywhere)

Question 21: Data consumers can first start to provide feedback to the current data sprint in the stakeholder review meeting.

  • True
  • False

Question 22: Which of the following could be found in catalog?

  • Code
  • Business terms
  • Data rules
  • Source data
  • Data lineage

Question 23: All issues need to be remediated before moving on to the next data sprint.

  • True
  • False

Question 24: Improvements to the DataOps process could involve changes to

  • Technology used in DataOps
  • DataOps team roles and responsibilities
  • Processes for ETL
  • All of the above

Question 25: Reviewing the Establish Baseline Process should include reviewing how effective are the processes for establishing a baseline for –

  • External Regulatory requirements
  • Organization maturity and Readiness
  • Governance and Oversight
  • All of the above

Introduction to DataOps Methodology

DataOps is a methodology that focuses on improving the speed and quality of data analytics and data-driven decision-making by integrating development, operations, and data science teams. Much like DevOps aims to streamline the software development lifecycle, DataOps applies similar principles to the data lifecycle.

Here’s a breakdown of some key components of the DataOps methodology:

  1. Collaboration: DataOps emphasizes collaboration and communication between different teams involved in the data lifecycle, such as data engineers, data scientists, analysts, and business stakeholders. This collaboration ensures that everyone is aligned on project goals and requirements.
  2. Automation: Automation plays a crucial role in DataOps. By automating repetitive tasks such as data ingestion, cleansing, transformation, and deployment, teams can reduce manual errors, increase efficiency, and accelerate the overall process of delivering insights.
  3. Agility: DataOps promotes an agile approach to data analytics. This involves breaking down large projects into smaller, manageable tasks or iterations, allowing teams to quickly adapt to changing requirements and deliver value incrementally.
  4. Continuous Integration and Deployment (CI/CD): Similar to DevOps practices, DataOps encourages continuous integration and deployment of data pipelines and analytics solutions. This means that changes to data pipelines are automatically tested and deployed, ensuring that they are always up-to-date and reliable.
  5. Monitoring and Feedback: DataOps teams continuously monitor the performance of data pipelines and analytics solutions to identify issues and optimize performance. Feedback loops are established to gather insights from end-users and stakeholders, which are used to improve future iterations.
  6. Quality Assurance: Ensuring the quality of data is crucial in DataOps. Quality checks and validation processes are integrated into data pipelines to detect and address issues such as data anomalies, inconsistencies, and inaccuracies.
  7. Security and Compliance: Data security and compliance with regulations such as GDPR, HIPAA, and CCPA are paramount in DataOps. Security measures such as encryption, access controls, and data anonymization are implemented to protect sensitive data and ensure regulatory compliance.

Overall, DataOps aims to create a culture of collaboration, automation, agility, and continuous improvement within data teams, ultimately enabling organizations to derive actionable insights from data more efficiently and effectively.

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