PDF4Test CPMAI_v7 Dumps Real Exam Questions Test Engine Dumps Training [Q47-Q69]

NEW QUESTION 47
You’re working with an inexperienced team and this is all their first AI project. You’re trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?

Pick a simple algorithm such as naive bayes

Pick a neural network algorithm since you know this works well for supervised learning approaches

Pick an ensemble method since you’re not sure which algorithm will perform best

Pick a simple algorithm such as Gaussian mixture

NEW QUESTION 48
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:

Make sure all senior leadership is data literate, understands the V’s of big data, data’s connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.

Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.

Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.

Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.

NEW QUESTION 49
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?

See if you can expand the scope to continue with the project

See if you already have access to enough data to continue with the project

See if you can adjust the scope of this interaction to continue with the project

See if you can purchase the data needed to continue with the project

NEW QUESTION 50
Your team is running a forecasting project and wants to use previous user data to better predict future outcomes. However your team doesn’t have access to all the data it needs. What’s the best course of action?

Move ahead as planned and hope you get access to the data once you need it. Since you’re using an iterative approach you can always go back to steps as needed later on.

Cautiously move forward knowing you may need to pause mid-project which is ok.

Move ahead as planned so you stay on time with your project.

Do not move forward until you have access to all the data you need.

NEW QUESTION 51
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

Data Governance

Data Integration

Data preparation

Data Mining or Data Analytics

NEW QUESTION 52
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?

Reinforcement Learning

Generative AI

Transfer Learning

Pre-Trained Models

NEW QUESTION 53
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What’s the most appropriate Model Operationalization approach?

Batch prediction

Real-time prediction

Web service / Microservice

Stream learning

NEW QUESTION 54
The confusion matrix measures how the algorithm performs for a binary classification activity. As your team is running tests to evaluate model performance, they are seeing the model is incorrectly categorizing flowers as trees. Your model is provided the following:

False Negative results

False Positive results

True Positive results

True Negative results

NEW QUESTION 55
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

Feature Engineering

Data Acquisition / Ingest / Capture

ELT pipeline

Retraining Pipelines

NEW QUESTION 56
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:

Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.

Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.

Make sure all senior leadership is data literate, understands the V’s of big data, data’s connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.

Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.

NEW QUESTION 57
Your team is looking for a short term ROI project and decides that an AI-enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition.
What’s the best course of action with how to proceed?

Do not move forward and cancel the project altogether.

Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go
/No Go decision chart to start your project.

Do not move forward until you can determine a clear problem definition.

Move forward with the project as planned. The problem definition will become clear later on in the project.

NEW QUESTION 58
Your model has been working fine for the last three months, however recently you notice the model’s performance has greatly declined. What seems to have been overlooked in your workflow pipeline?

Model retraining

Model Operationalization

Model Drift

Model reevaluation

NEW QUESTION 59
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?

They did not have data governance in place

They did not practice model versioning and keep all versions of the model

They did not have a model retraining pipeline that took into account models

They did not practice model iteration and properly iterate on the model

NEW QUESTION 60
Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project.
What type of algorithm would you select for this problem?

Clustering

Regression

Binary (or Binomial) Classification

Multiclass Classification

NEW QUESTION 61
You’re working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can’t wait weeks until a data science resource becomes available to join your team.
What’s your best course of action?

Outsource the project ASAP

Find a citizen data scientist to help

Put the project on hold until the resources needed become available

Use an AutoML solution

NEW QUESTION 62
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What’s the most critical role to staff for in the Big Data / Data Engineering Environment?

Data Scientists

Data Engineering and Data Scientists

Senior management

Data Engineering

All roles are critical to staff in the Four different AI Tech environments

NEW QUESTION 63
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

Descriptive Analytics

Predictive Analytics

Diagnostic Analytics

Projective Analytics

NEW QUESTION 64
Which of the following best describes the technical definition of Machine Learning?

An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

The application of pre-defined rules and algorithms to solve complex problems.

The use of computing technology to enable machines to gain cognitive intelligence.

NEW QUESTION 65
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

The algorithm code

Supporting training data

Bias-variance tradeoff

Hyperparameter settings

NEW QUESTION 66
Use cognitive technologies/AI when you can’t code the rules or you can’t scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

Decide if it’s a statistics pattern. If it’s statistical then go with the AI project.

Decide if it’s probabilistic or deterministic patterns. If it’s deterministic then go with the AI project.

See if simple rules work. If yes, then pick the right AI solution to solve the problem.

Decide if it’s probabilistic or deterministic patterns. If it’s probabilistic then go with the AI project.

NEW QUESTION 67
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don’t have enough data to train the model.
What’s the best course of action?

Research what Third-Party Models are available and purchase them to keep the project moving

Do not move forward with the project

Ask your customer service team to generate additional data for you to use for the project

Use the data that you have and keep the project moving

NEW QUESTION 68
When looking to implement AI to help break the Digital Transformation logjam, it’s important to:

Have the right culture for AI

Invest in technology solutions to solve the problem

Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes

Start with basic automation and introduce AI only as needed

NEW QUESTION 69
You’re testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?

You are underfitting the data

You are overfitting the data

You have selected the wrong algorithm

You have selected the wrong data

Free certification test prep

PDF4Test CPMAI_v7 Dumps Real Exam Questions Test Engine Dumps Training [Q47-Q69]

Leave a Reply Cancel reply