AIP-210 Practice Questions

Video tracking is a technique that involves detecting and following moving objects in a video

sequence. Video tracking can be used for various applications, such as surveillance, security, sports

analysis, and human-computer interaction. One typical use case of video tracking is traffic

monitoring, where video tracking can help measure traffic flow, detect congestion, identify

violations, and optimize traffic signals.

Semi-supervised learning is an approach that is best if a limited portion of your training data is

labeled. Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled

data to train a model. Semi-supervised learning can leverage the large amount of unlabeled data that

is easier and cheaper to obtain and use it to improve the model’s performance. Semi-supervised

learning can use various techniques, such as self-training, co-training, or generative models, to

incorporate unlabeled data into the learning process.

Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive

cases). Precision is calculated by dividing the number of true positives (correctly predicted positive

cases) by the number of predicted positive cases (true positives and false positives). In this confusion

matrix, the true positives are 37 and the false positives are 8, so the precision is 37/(37+8) = 0.822.

Completeness is a quality criterion that describes the degree to which all required measures are

known. Completeness can help assess the coverage and availability of data for a given purpose or

analysis. Completeness can be measured by comparing the actual number of measures with the

expected number of measures, or by identifying and counting any missing, null, or unknown values

in the data.

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.

Security measures can be applied in different ML workflow stages to defend them against malicious

activities, such as data theft, model tampering, or adversarial attacks. Some of the security measures

are:

Launch ML Instances In a virtual private cloud (VPC): A VPC is a logically isolated section of a cloud

provider’s network that allows users to launch and control their own resources. By launching ML

instances in a VPC, users can enhance the security and privacy of their data and models, as well as

restrict the access and traffic to and from the instances.

Use data encryption: Data encryption is the process of transforming data into an unreadable format

using a secret key or algorithm. Data encryption can protect the confidentiality, integrity, and

availability of data at rest (stored in databases or files) or in transit (transferred over networks). Data

encryption can prevent unauthorized access, modification, or leakage of sensitive data.

Use Secrets Manager to protect credentials: Secrets Manager is a service that helps users securely

store, manage, and retrieve secrets, such as passwords, API keys, tokens, or certificates. Secrets

Manager can help users protect their credentials from unauthorized access or exposure, as well as

rotate them automatically to comply with security policies.

Encoding is a technique that transforms categorical data into numerical features that can be used by

machine learning models. Categorical data are data that have a finite number of possible values or

categories, such as gender, color, or country. Encoding can help convert categorical data into a

format that is suitable and understandable for machine learning models. Some of the encoding

methods that can be used to transform categorical data into numerical features are:

Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the

target variable for that category. Mean encoder can capture the relationship between the category

and the target variable, but it may cause overfitting or multicollinearity problems.

One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where

only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can

create distinct and orthogonal vectors for each category, but it may increase the dimensionality and

sparsity of the data.

:

Workflow design patterns for machine learning pipelines are common solutions to recurring

problems in building and managing machine learning workflows. One of these patterns is to

represent a pipeline with a directed acyclic graph (DAG), which is a graph that consists of nodes and

edges, where each node represents a step or task in the pipeline, and each edge represents a

dependency or order between the tasks. A DAG has no cycles, meaning there is no way to start at

one node and return to it by following the edges. A DAG can help visualize and organize the pipeline,

as well as facilitate parallel execution, fault tolerance, and reproducibility.

An incident database is a database that is designed to better anticipate and avoid risks of AI systems

causing safety, fairness, or other ethical problems. An incident database collects and stores

information about incidents or events where AI systems have caused or contributed to negative

outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident

database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents,

as well as provide guidance and best practices for preventing or mitigating future incidents.