Question # 1
In the machine learning context, feature engineering is the process of?
| A. Converting raw data into clean data.
| B. Creating learning schema for a model apply.
| C. Developing guidelines to train and test a model.
| D. Extracting attributes and variables from raw data.
|
D. Extracting attributes and variables from raw data.
Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively.
Reference:
AIGP Body of Knowledge on AI Model Development and Feature Engineering.
Question # 2
Random forest algorithms are in what type of machine learning model?
| A. Symbolic.
| B. Generative.
| C. Discriminative.
| D. Natural language processing.
|
C. Discriminative.
Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms. They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
[Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks., , ]
Question # 3
A company initially intended to use a large data set containing personal information to train an Al model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?
| A. Anonymization.
| B. Pseudonymization.
| C. Differential privacy.
| D. Federated learning.
|
A. Anonymization.
Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset.
Reference:
AIGP Body of Knowledge, privacy-enhancing techniques section.
Question # 4
What is the best reason for a company adopt a policy that prohibits the use of generative Al?
| | A. Avoid using technology that cannot be monetized. | B. Avoid needing to identify and hire qualified resources.
| C. Avoid the time necessary to train employees on acceptable use.
| D. Avoid accidental disclosure to its confidential and proprietary information.
|
D. Avoid accidental disclosure to its confidential and proprietary information.
Explanation:
The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company’s competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.
Question # 5
You are part of your organization’s ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating. What is the best first step address this?
| A. Replace the model with a previous version.
| B. Conduct champion/challenger testing.
| C. Perform an audit of the model.
| D. Run red-teaming exercises.
|
B. Conduct champion/challenger testing.
Explanation:
When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.
[Reference: AIGP BODY OF KNOWLEDGE, sections on model performance management and testing strategies., , ]
Question # 6
| Training data is best defined as a subset of data that is used to? | A. Enable a model to detect and learn patterns.
| B. Fine-tune a model to improve accuracy and prevent overfitting.
| C. Detect the initial sources of biases to mitigate prior to deployment.
| D. Resemble the structure and statistical properties of production data.
|
A. Enable a model to detect and learn patterns.
Explanation:
Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.
Question # 7
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?
| A. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
| B. Momentum, which improves the convergence speed and stability of neural network training.
| C. Autoregression, which analyzes and makes predictions about time-series data.
| D. Backpropagation, which starts from the last layer working backwards.
|
C. Autoregression, which analyzes and makes predictions about time-series data.
Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization. Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.
IAPP AIGP Exam Dumps
5 out of 5
Pass Your Artificial Intelligence Governance Professional Exam in First Attempt With AIGP Exam Dumps. Real Artificial Intelligence Governance Exam Questions As in Actual Exam!
— 100 Questions With Valid Answers
— Updation Date : 16-Dec-2024
— Free AIGP Updates for 90 Days
— 98% Artificial Intelligence Governance Professional Exam Passing Rate
PDF Only Price 99.99$
19.99$
Buy PDF
Speciality
Additional Information
Testimonials
Related Exams
- Number 1 IAPP Artificial Intelligence Governance study material online
- Regular AIGP dumps updates for free.
- Artificial Intelligence Governance Professional Practice exam questions with their answers and explaination.
- Our commitment to your success continues through your exam with 24/7 support.
- Free AIGP exam dumps updates for 90 days
- 97% more cost effective than traditional training
- Artificial Intelligence Governance Professional Practice test to boost your knowledge
- 100% correct Artificial Intelligence Governance questions answers compiled by senior IT professionals
IAPP AIGP Braindumps
Realbraindumps.com is providing Artificial Intelligence Governance AIGP braindumps which are accurate and of high-quality verified by the team of experts. The IAPP AIGP dumps are comprised of Artificial Intelligence Governance Professional questions answers available in printable PDF files and online practice test formats. Our best recommended and an economical package is Artificial Intelligence Governance PDF file + test engine discount package along with 3 months free updates of AIGP exam questions. We have compiled Artificial Intelligence Governance exam dumps question answers pdf file for you so that you can easily prepare for your exam. Our IAPP braindumps will help you in exam. Obtaining valuable professional IAPP Artificial Intelligence Governance certifications with AIGP exam questions answers will always be beneficial to IT professionals by enhancing their knowledge and boosting their career.
Yes, really its not as tougher as before. Websites like Realbraindumps.com are playing a significant role to make this possible in this competitive world to pass exams with help of Artificial Intelligence Governance AIGP dumps questions. We are here to encourage your ambition and helping you in all possible ways. Our excellent and incomparable IAPP Artificial Intelligence Governance Professional exam questions answers study material will help you to get through your certification AIGP exam braindumps in the first attempt.
Pass Exam With IAPP Artificial Intelligence Governance Dumps. We at Realbraindumps are committed to provide you Artificial Intelligence Governance Professional braindumps questions answers online. We recommend you to prepare from our study material and boost your knowledge. You can also get discount on our IAPP AIGP dumps. Just talk with our support representatives and ask for special discount on Artificial Intelligence Governance exam braindumps. We have latest AIGP exam dumps having all IAPP Artificial Intelligence Governance Professional dumps questions written to the highest standards of technical accuracy and can be instantly downloaded and accessed by the candidates when once purchased. Practicing Online Artificial Intelligence Governance AIGP braindumps will help you to get wholly prepared and familiar with the real exam condition. Free Artificial Intelligence Governance exam braindumps demos are available for your satisfaction before purchase order.
Send us mail if you want to check IAPP AIGP Artificial Intelligence Governance Professional DEMO before your purchase and our support team will send you in email.
If you don't find your dumps here then you can request what you need and we shall provide it to you.
Bulk Packages
$60
- Get 3 Exams PDF
- Get $33 Discount
- Mention Exam Codes in Payment Description.
Buy 3 Exams PDF
$90
- Get 5 Exams PDF
- Get $65 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF
$110
- Get 5 Exams PDF + Test Engine
- Get $105 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF + Engine
 Jessica Doe
Artificial Intelligence Governance
We are providing IAPP AIGP Braindumps with practice exam question answers. These will help you to prepare your Artificial Intelligence Governance Professional exam. Buy Artificial Intelligence Governance AIGP dumps and boost your knowledge.
|
