QUESTION: 17

Which of the following is the definition of accuracy?

(True Positives + False Positives) / Total Predictions
(True Positives + True Negatives) / Total Predictions
True Positives / (True Positives + False Negatives)
True Positives / (True Positives + False Positives)

Answer(s): B

Explanation:

Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).

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QUESTION: 18

Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)

If it is for a good cause.
If it was collected accidentally.
If it was requested by the authority of law.
If it was with consent of the person it is collected from.
If the data is only collected once.

Answer(s): C,D

Explanation:

Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button. The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.

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QUESTION: 19

Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?

Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.

Answer(s): D

Explanation:

Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider's infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more. Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results. Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications. Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.

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QUESTION: 20

In a self-driving car company, ML engineers want to develop a model for dynamic pathing.
Which of following approaches would be optimal for this task?

Dijkstra Algorithm
Reinforcement learning
Supervised Learning.
Unsupervised Learning

Answer(s): B

Explanation:

Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment. Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .

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Free AIP-210 Exam Braindumps (page: 5)

QUESTION: 17

Explanation:

QUESTION: 18

Explanation:

QUESTION: 19

Explanation:

QUESTION: 20

Explanation: