APMG International DLFU Certification Exam Syllabus

DLFU dumps PDF, APMG International DLFU Braindumps, free Data Literacy Fundamentals dumps, Data Literacy Fundamentals dumps free downloadTo achieve the professional designation of APMG International Data Literacy Fundamentals from the APMG International, candidates must clear the DLFU Exam with the minimum cut-off score. For those who wish to pass the APMG International Data Literacy Fundamentals certification exam with good percentage, please take a look at the following reference document detailing what should be included in APMG International Data Literacy Fundamentals Exam preparation.

The APMG International DLFU Exam Summary, Body of Knowledge (BOK), Sample Question Bank and Practice Exam provide the basis for the real APMG International Certified Data Literacy Fundamentals (DLFU) exam. We have designed these resources to help you get ready to take APMG International Data Literacy Fundamentals (DLFU) exam. If you have made the decision to become a certified professional, we suggest you take authorized training and prepare with our online premium APMG International Data Literacy Fundamentals Practice Exam to achieve the best result.

APMG International DLFU Exam Summary:

Exam Name APMG International Data Literacy Fundamentals
Exam Code DLFU
Exam Fee USD $308
Exam Duration 60 Minutes
Number of Questions 40
Passing Score 65%
Format Multiple Choice Questions
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Sample Questions APMG International Data Literacy Fundamentals Exam Sample Questions and Answers
Practice Exam APMG International Certified Data Literacy Fundamentals (DLFU) Practice Test

APMG International Data Literacy Fundamentals Syllabus Topics:

Topic Details
Understand the importance of data literacy
- Describe the different components of data literacy:
  • Data access
  • Data understanding
  • Data analysis
  • Data visualization
  • Ethical considerations
- Describe the different levels of data literacy In organizations:
  • Data unaware
  • Data aware
  • Data capable
  • Data proficient
  • Data-driven
- Describe the key benefits of the data-driven organization
- Describe different personas in data literacy and their functions and objectives:
  • Data novice
  • Data explorer
  • Data analyst
  • Data communicator
  • Data steward
Understand fundamental data concepts
- Describe different data types and their objectives:
  • Numerical data
  • Categorical data
  • Textual data
  • Image data
  • Audio data
  • Time-series data
  • Geo-spatial data
  • Sensor data
- Describe the different structure and objectives of data structures:
  • Structured data
  • Unstructured data
  • Metadata
- Describe different types of metadata:
  • Descriptive metadata
  • Administrative metadata
  • Technical metadata
  • Structural metadata
- Recall critical definitions that define data quality:
  • Accuracy
  • Completeness
  • Consistency
  • Validity
  • Timeliness
  • Uniqueness
- Describe different storage mechanisms and their purpose in storing data:
  • Local storage
  • Cloud storage
  • Databases
  • Data warehouses
  • Data lake
- Recall common data security measures:
  • Access controls
  • Encryption
  • Firewalls
  • Intrusion detection
  • Incident response
- Recall common data privacy regulations:
  • GDPR
  • CCPA
  • HIPAA
- Describe the different steps in the data analysis process:
  • Define problem objectives
  • Collect and prepare data
  • Explore data
  • Model data
  • Interpret the results
  • Communicate and collaborate
  • Implement insights
  • Review and monitor
- Describe common data analysis tools and technologies:
  • Excel
  • R and Python
  • SQL
Understand how to describe data sets
- Recall basic definitions of data sets:
  • Variables
  • Values
  • Observations
  • Population
  • Sample
  • Data provenance
- Describe the different measures of central tendency:
  • Mean
  • Median
  • Mode
- Describe the different measures of variability:
  • Range
  • Variance
  • Standard deviation
- Describe the different measures of shape:
  • Symmetry
  • Skewness
  • Kurtosis
- Describe the most common data cleaning techniques:
  • Duplicate records removal
  • Missing values imputation
  • Outlier detection and correction
- Describe the objective and purpose of data wrangling
- Describe the different types of bias:
  • Sampling bias
  • Selection bias
  • Confirmation bias
  • Measurement bias
Understand and interpret data visualizations
- Recall the most common data visualization techniques for organizations:
  • Bar charts
  • Histograms
  • Line graphs
  • Scatter plots
  • Pie charts
  • Box plots
  • Heat Maps
  • Maps
- Describe the nature, use and purpose of the following data visualization techniques:
  • Bar charts
  • Histograms
  • Line graphs
  • Scatter plots
  • Pie charts
  • Box plots
  • Heat Maps
  • Maps
Understand how to use data to explain a story
- Recall the core steps of Storytelling with Data:
  • Understand the audience
  • Collecting and organizing data
  • Chose the right visualizations
  • Build the story with data
  • Design the story with data
  • Present the story
- Recall the core components of an effective data story:
  • Clear goal
  • Well-defined audience
  • Relevant and meaningful data
  • Clear structure
  • Effective visualization
  • A clear message
  • Emotional connection
  • Actionable insights
  • Engaging presentation
- Describe the elements of clear and effective visualizations:
  • Plan the layout
  • Choose the right type of visualization
  • Keep it simple
  • Use the appropriate scales
  • Label axis and add captions
  • Use colors and fonts effectively
  • Provide context
- Describe the most common data storytelling techniques:
  • Character development
  • Conflict and resolution
  • Setting
  • Plot structure
  • Dialogue
  • Point of view
  • Symbolism and metaphor
  • Foreshadowing and suspense
Understand data ethics and their implications
- Recall the importance of data ethics:
  • Legal compliance
  • Brand reputation
  • Risk Management
  • Innovation
  • Respect for individual rights
- Describe the five principles of data ethics:
  • Respect for persons
  • Beneficence
  • Non-Maleficence
  • Justice
  • Transparency
- Describe the purpose, objective, and ethical principles of data privacy
- Describe the core components of a data privacy policy:
  • Description of personal information
  • Purpose of collection
  • Collection and use
  • Sharing
  • Security
  • Access and correction
- Describe the purpose, objective and importance of data ethics policies and procedures
- Describe enforcement mechanism of data ethics policies:
  • Training and awareness
  • Auditing and monitoring
  • Incident response and remediation
  • Accountability and consequences
  • Continuous improvement
- Describe the purpose, objective, and importance of the data ethics committee

Both APMG International and veterans who’ve earned multiple certifications maintain that the best preparation for a APMG International DLFU professional certification exam is practical experience, hands-on training and practice exam. This is the most effective way to gain in-depth understanding of APMG International Data Literacy Fundamentals concepts. When you understand techniques, it helps you retain APMG International Data Literacy Fundamentals knowledge and recall that when needed.

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