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Examquestions Flashcards

1
Q

What is Artificial Intelligence?

A

Machines that perform jobs that mimic human behavior

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2
Q

What is Machine Learning?

A

Machines that get better at a task without explicit programming

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3
Q

What is deep learning?

A

Machines that have an artificial neural network inspired by the human brain to solve complex problems

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4
Q

What are key elements to AI?

A

Machine Learning: foundation of an AI system to learn and predict human behaviour

Anomaly detection: detect outliers

Computer Vision: see like a human

Natural language processing: process human language with context

Conversational AI: hold a conversation like a human

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5
Q

What is a dataset?

A

logical grouping of units of data that have the same data structure to train

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6
Q

What is Data labeling?

A

identifying raw data and adding labels to provide context to help the machine learn

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7
Q

What is Supervised Learning?

A

You know the labels and have a precise output you want

make a prediction

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8
Q

What is Unsupervised learning?

A

labels are not known and the outcome does not need to be precise

recognize a structure or pattern

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9
Q

What is Reinforcement Learning?

A

No data and a ML model generates data to reach a goal

Game AI

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10
Q

What are Neural Networks?

A

algorithm to represent a brain
connections between neurons are weighted with multiple layers

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11
Q

What is deep learning?

A

A neural network with 3 or more layers

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12
Q

What is feed forward?

A

Neural Network where connections between nodes dont form a cycle

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13
Q

What is backpropagation? (BP)

A

backward movement though a neural network to adjust weights to improve the outcome on the next iteration - this is how it learns

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14
Q

What is the ground truth?

A

the answer/reality you want to reach with the model

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15
Q

What is a loss function?

A

compares the ground truth to the prediction to determine the error rate

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16
Q

Why are GPUs used for machine learning?

A

they can perform parallel operations on multiple sets of data - can have thousands or processor cores

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17
Q

What is forecasting?

A

makes a future prediction with relevant data

“analysis of trends”

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18
Q

What is a prediction?

A

makes a future prediction without relevant data

uses statistics to predict future outcomes

more guessing than forecasting

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19
Q

What are internal Evaluation metrics for an ML model?

A

Accuracy, F1 score, Precision, Recall

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20
Q

What are the external Evaluation metrics for an ML model?

A

metrics used to evaluate the final prediction of the ML model

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21
Q

What is Jupyter Notebook/Lab?

A

web-based application for authoring documents with live-code
text
visualizations etc.

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22
Q

What is Regression?

A

process of finding a function to correlate a labeled labeled dataset into continuous variable/number - make a prediction into the future

distance from the vector to the regression line is called error

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23
Q

What is classification?

A

finding a function to divide a labeled dataset into classes/categories

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24
Q

What is clustering?

A

grouping unlabeled data based on similarities and differences

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25
What is a confusion matrix?
visualize the model predictions vs ground truth labels (actual) also called error matrix
26
What Anomaly detection?
process of finding an outlier in a dataset
27
What is Computer Vision?
gain level of understanding digital images and video Convolutional neural network: image and video recognition recurrent neural network: handwriting or speech recognition Semantic Segmentation: identify segments or objects Image analysis: analyse image or video and apply label
28
What is natural language processing?
understand the context of a corpus analyzes text and interpret etc.
29
What is conversational AI?
Chatbots, Voice Assistants etc. Online Customer Support HR processes Health Care IoT etc.
30
What is Responsible AI? What are the principles?
ethical, transparent and accountable AI 1. Fairness - should treat all people fairly 2. Reliability and Safety 3. Privacy and Security 4. Inclusiveness 5. Transparency 6. Accountability
31
What is Azure Cognitive Services?
comprehensive family of AI services and cognitive APIs to help you build intelligent apps deploy with containers, with not much expertise
32
What is part of Azure Cognitive Services?
Decision: Anomaly Detector Content Moderator Personaliser Language: Language Understanding QnA Maker Text Analytics Translator Speech: Speech to Text Text to Speech Speech Translation Speaker Recognition Vision: Computer Vision Custom Vision Face Detection
33
What is Knowledge Mining?
Knowledge Mining is a discipline that uses a combination of intelligent services to quickly learn from vast amounts of information
34
What are text analytics and what is part of it?
Sentiment Analysis Opinion Mining Key Phrase Extradiction Language Detection Named entity recognition
35
What are important infos to the Form Recognizer Service?
trained with own data only needs five sample input forms to start can use it to extract data from forms
36
What are the key parts LUIS reads out of a text?
Intention Entities what parts of the intent is used to determine the answer utterance: the user input
37
What is the Azure Bot Service?
Intelligent, serverless bot service that scales on demand - is used for creating, publishing and managing bots
38
What is Bot Framework Composer?
opensource IDE for developers to create conversational experiences (bots) has templates and can author, test, provision and manage solutions
39
What is Azure Machine Learning Services?
Service to run workloads Jupyter Notebooks Azure Machine Learning Python: code MLOps: automation for creation Azure Machine Learning Designer: no code Data Labeling Service: ask humans to label data Responsible Machine Learning
40
Whats part of author in Azure Machine Learning Studio?
Notebooks: write python code to built models AutoML: auto building of models (limited) Designer: No Code Designer to construct ML
41
Whats part of Assets in Azure Machine Learning Studio?
Datasets Experiments Pipelines Models Endpoints
42
What is part of "manage" in Azure Machine Learning Studio?
Compute Environments Datastores Data Labeling Linked Services
43
What are the four kinds of compute to choose in Azure Machine Learning Studio?
Compute Instances: development workstations Compute Clusters: scalable virtual machines Inference Clusters: deployment targets for services Attached Compute: links to exisiting azure resources
44
What options are there for labeling in Machine Learning Studio?
Human labeling Machine learning assisted data labeling
45
What are the two ways to build pipelines?
Azure Machine Learning Designer Azure Machine Learning Python SDK
46
What is Model Registry?
management tool for your models
47
What is Azure ML Endpoints?
service that allows you to deploy machine learning models as a web service
48
If you deploy a model over Endpoints what Compute types are used?
Containers or Kubernetes
49
What is AutoML?
automates the creation of an ML model
50
What is Data Guard Rails in AutoML
service that ensures high quality for the input data
51
What scaling or normalization techniques are available in AutoML?
StandardScaleWrapper: normalizes values for better performance MinMaxScalar: transforms features according to min and max value RobustScalar: Scales features by their quantile range Principal component analysis: reduces the labels TruncatedSVDWrapper: reduces complexity but without centering the data first SparseNormalizer: similar
52
What is the Model Selection within AutoML?
service that helps you select the best performing statistical model
53
What are classification metrics?
For well-balanced datasets: accuracy average_precision_score_weighted norm_macro_recall precision_score_weighted imbalanced: AUC_weighted
54
What is a well-balanced/imbalanced dataset?
well-balanced: evenly distributed values imbalanced: some values very often, others are scarce
55
What are primary metrics for Regression Scenarios?
large range: spearman_correlation r2_score small range: normalized_root_mean_squared_error normalized_mean_absolute_error
56
What is Model Validation?
Compares how good the model "learned" by comparing the results from a training dataset with a test dataset
57
What is custom vision?
no-code service to build Classification and Object Detection ML Models
58
Custom Vision - What Classification Domains are recommended for which size of datasets?
bigger datasets: General General A1 smaller datasets: General A2
59
Custom Vision - How many images do you need per label to train?
15
60
Custom Vision - What are evaluation metrics?
Precision: accuracy Recall: How many relevant iteams returned? Average Precision For object detection: mean average precision
61
What should Kubernetes and what should Containers be used for?
Kubernetes: Deployment Containers: Dev/Testing
62
What are labels and features?
Feature: Nebenwerte um auf den zu schätzenden Wert zu schließen Label: der Wert der geschätzt wird
63
What is the difference between clustering and categorizing?
Cluster: ähnliche Werte zusammen in Kategorien bringen Categorizing: Werte anhand von festen Labeln zuordnen
64
Was sind Metriken um Regression zu bewerten?
R2 RMSE
65
What Metric for classification models?
true positive rate
66
Kann Custom Vision service für Videos benutzt werden?
Nein
67

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