Exam: CPMAI_v7
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The CPMAI v7 exam, developed by Project Management Institute (PMI) and
Cognilytica, is a 100-question, multiple-choice exam assessing a candidate's
understanding of AI project management methodologies. It doesn't require
prerequisites, and focuses on applying CPMAI concepts rather than basic
knowledge. The exam is 2 hours long and the passing score is 70%. It is
administered online, and currently does not require proctoring.
Key Details:
Format: 100 multiple-choice questions.
Duration: 120 minutes (2 hours).
Passing Score: 70%.
Language: English.
Delivery: Online, self-directed, and not proctored.
Content: Focuses on applying the CPMAI methodology and AI project management
concepts.
No Prerequisites: The certification does not require prior experience, technical
knowledge, or AI expertise.
Retake Policy: One free retake
Administered by: Cognilytica and included with course enrollment.
What you'll learn
Fundamentals of Big Data
Build a strong foundation in Big Data, exploring its characteristics,
challenges, and role in AI-driven decision-making to maximize the value of
data-driven projects.
Big Data Platforms
Explore the approaches used to store, process, and analyze large-scale data,
from cloud solutions to distributed computing frameworks.
Foundations of Data Science
Gain a solid understanding of data science principles, methodologies, and
how they’re leveraged in AI projects and applied to extract insights from data.
Thinking & Acting Like a Data Scientist
Develop the mindset and approach of a data scientist, focusing on
problem-solving, experimentation, and data-driven decision-making.
Foundations of Robotic Process Automation (RPA)
Discover the basics of RPA, including how automation improves efficiency and
streamlines business processes.
Applications of RPA
Explore real-world use cases of RPA across industries and learn how
automation is transforming business operations.
Big Data Analytics & Visualization
Learn how to analyze large datasets and effectively visualize data to
communicate insights and drive business strategies.
Big Data Engineering
Understand the technical aspects of designing, building, and maintaining
scalable data infrastructure for AI and analytics.
Big Data Security & Governance
Dive into best practices for securing Big Data, ensuring compliance, and
implementing data governance strategies.
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QUESTION 1
Your team is working on an NLP model and has just operationalized the first
model.
Your team makes updates to the model, overwrites the original model, and puts
this new model into operation.
However, one of the teams using the model has seen a decrease in performance and
is asking to use the original model.
What critical error did your team make?
A. They did not have data governance in place
B. They did not practice model versioning and keep all versions of the model
C. They did not have a model retraining pipeline that took into account models
D. They did not practice model iteration and properly iterate on the model
Answer: B
QUESTION 2
Enhancing and cleaning data is an important action during which phase of
CPMAI?
A. Phase VI
B. Phase I
C. Phase V
D. Phase III
E. Phase II
F. Phase IV
Answer: D
QUESTION 3
Your team is ready to operationalize the model they have been working on.
It's a model that is meant to be used on an "edge device", specifically a mobile
phone and the user may sometimes be in
remote locations without regular access to the internet.
What's the most important thing to consider here?
A. Make sure that you can use Generative AI solutions on an edge device
B. Make sure the model lives in a hybrid environment
C. Make sure the model is available over a cloud-based API
D. Make sure the model lives on the edge device so it can be used regardless of
internet connection
Answer: D
QUESTION 4
For AI projects the code and systems don't matter as much as the data.
In fact, big data is what's powering much of this latest wave of AI. What's most
important for your company to consider around data?
A. Because of almost-infinite storage and compute power, collect as much data as
possible and deal with organizing it later.
B. Collect enormous amounts of data - the more data the better.
C. Understanding which algorithms are best for your data needs.
D. Have team members that have experience, understanding of tools, and the
ability to deal with massive volumes of data.
Answer: C
QUESTION 5
Using machine learning and other cognitive approaches to understand how to
take past/existing
behavior and predict future outcomes or help humans make decisions about future
outcomes using
insight learned from past behavior/interactions/data is a core part to which
pattern(s) of AI?
A. Goal Driven Systems
B. Predictive Analytics & Decision Support and Patterns and Anomalies
C. Recognition Pattern
D. Predictive Analytics & Decision Support
Answer: D
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