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Brainscape_all_20190317 Flashcards

1
Q

Overnormalization

A

d

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2
Q

1 Byte

A

0 to 255 (8 bit)

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3
Q

SQL Lock

A

J

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4
Q

Entity

A

Tables

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5
Q

Scale (Data)

A

Number of decimal places (123.123 = 3)

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6
Q

Parse()

A

Convert strings to dates.

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7
Q

Cache

A

d

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8
Q

∅

A

Empty Set

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9
Q

Overfitting

A

d

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10
Q

Foreign Key

A

Links to the primary key of another table.

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11
Q

Referential Integrity

A

(Order for employee that doesn’t exist)

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12
Q

Superlative

A

An exaggerated or hyperbolical expression of praise.

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13
Q

Denormalization

A

SAMS vs RAD

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14
Q

Dimension Table

A

d

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15
Q

Floating Point Number

A

Efficient, but lose accuracy.

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16
Q

CREATE SEQUENCE

A

D

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17
Q

Ridge Regression

A

f

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18
Q

1st Normal Form

A

No repeating columns.

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19
Q

Table Scan

A

Scans entire table contents.

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20
Q

Try_Cast()

A

Same as Cast, but generates nulls for errors. (Cast/Convert/Parse)

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21
Q

Accuracy

A

d

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22
Q

clf

A

Classifier

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23
Q

Candidate Key

A

Possible primary keys.

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24
Q

EDH

A

Enterprise Data Hub

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25
Q

Don’t 1

A

Meddle

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26
Q

Clustered Index

A

Pyramid

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27
Q

API

A

Application Program Interface.

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28
Q

Crossvalidation

A

Randomly breaks data into parts (80/20 training/testing).

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29
Q

Hyperbole

A

Exaggerated statements or claims not meant to be taken literally.

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30
Q

Uniqueness Constraint

A

Generally covered by primary key, but could also be valid for surrogate keys.

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31
Q

Windowed Functions Syntax

A

Function() Over (Partition By x,y,z Order By a,b,c,x,y,z)

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32
Q

Data Integrity

A

d

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33
Q

Attributes

A

Columns

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34
Q

.iloc

A

d

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35
Q

Use full object name.

A

Servicer/Database/Schema/Object. Improves performance because SQL doesn’t have to figure out.

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36
Q

Checkpoint

A

d

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37
Q

Forms

A

Every attribute must provide a fact about the key, the whole key, and nothing but they key, so help me Codd.

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38
Q

Density(Index)

A

Uniqueness

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39
Q

K-fold Cross Validation

A

d

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40
Q

2 Byte

A

+-30,000 (smallint)

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41
Q

Don’t 3

A

Accept, reject, turn away.

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42
Q

.head()

A

Number of rows to select (pandas dataframe).

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43
Q

Sword of Damocles

A

Representing an impending ever-present threat. Especially to someone in a position of power.

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44
Q

8? Byte

A

bigint (64 bit)

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45
Q

GUID

A

Global Unique ID

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46
Q

Create Procedure

A

CREATE PROCEDURE schema.name AS …; …; go EXECUTE schema.name (@=x)

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47
Q

Overtesting

A

d

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48
Q

3rd Normal Form

A

All fields must only depend on a key. (3NF)

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49
Q

Recall

A

d

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50
Q

Ontology

A

The study of existence, reality, being.”What can be said to exist?”“What is a thing?”“Into what categories, if any, can we sort existing things?”“What are the meanings of being?”

51
Q

Universal No Lock

A

SET TRANSACTION LEVEL READ COMMITED

52
Q

Fixed Precision

A

Decimal/Numeric. More costly. More precise/accurate than float.

53
Q

Type 1 error

A

False positive

54
Q

Confusion Matrix

A

d

55
Q

Overfitting

A

Overly complex model fits original data very closely, but is poor at making predictions from other data sets, because irrelevant connections are made.

56
Q

Normalization

A

d

57
Q

Selectivity(Index)

A

By how much does the field reduce the values to be searched.

58
Q

Acclimate

A

Become accustomed to a new climate or to new conditions.

59
Q

Precision

A

d

60
Q

Primary Key

A

Unique value per row. Can be made up of multiple columns (composite) key. Functions as a target which a foreign key can reference.

61
Q

Random Seed

A

f

62
Q

Obsequious

A

Excessively obedient or attentive. Sycophantic.

63
Q

Depth(Index)

A

d

64
Q

Data Type Command

A

Type(x)

65
Q

Exists

A

Returns True/False

66
Q

⋃

A

Union

67
Q

Epistemology

A

Relating to the theory of knowledge. What does it mean to know something. How do we know what we know.

68
Q

Entity Integrity

A

Looks across columns. Uniqueness.

69
Q

Dependency

A

Columns retrievable by key? In order to lookup a value, you have to know the key first.

70
Q

OLAP Cube

A

Pre-groups to save time.

71
Q

Transaction(db)

A

A sequence of database operations that satisfies the ACID properties (can be perceived as a single logical operation on the data).

72
Q

ODS

A

Operational Data Store

73
Q

Leaf Level (Index)

A

All the data.

74
Q

Overtraining

A

d

75
Q

Probability Axioms

A

P(A) ≥ 0P(Ω) = 1If P(A⋂B) = ∅ THEN P(A⋃B) = P(A)+ P(B)

76
Q

Fact Table

A

d

77
Q

String Values

A

Larger because there are way more than 9(10?) values per character.

78
Q

Connect to Python

A

import pyodbcconn = pyodbc.connect(‘Driver={SQL Server};’ ‘Server=he2qntvpsql382;’ ‘Database=MHASAMS01PDB;’ ‘Trusted_Connection=yes;’)cursor = conn.cursor()cursor.execute(‘SELECT Organization FROM MHASAMS01PDB.ra.OrgLookup’)for row in cursor: print(row)

79
Q

Help Command

A

Help(x)

80
Q

Duplication

A

Attribute unnecessarily repeated. (SAMS and RAD duplication.)

81
Q

Json

A

Identifies data types auto

82
Q

OLAP

A

Online Analytical ProcessingOptimized for read only. Good for analytics.

83
Q

Disjoint

A

P(A⋂B) = ∅

84
Q

Batch Gradient Descent

A

d

85
Q

SVD Decomposition

A

d

86
Q

obsequious

A

obedient or attentive to an excessive or servile degree.”they were served by obsequious waiters”

87
Q

Schema

A

Groupings of objects (tables). Can grants permissions based on schemas.

88
Q

Who’s causing you harm?

A

You are.

89
Q

Default Value For Data Integrity

A

k today(), getdate()

90
Q

EDW

A

Enterprise Data Warehouse

91
Q

OLTP

A

Online Transaction ProcessingTypically facilitate and manage transaction-oriented (UPDATE, INSERT, DELETE, ALTER) applications vs analytical (OLAP).

92
Q

⋂

A

Intersection

93
Q

2nd Normal Form

A

Non-key columns shouldn’t be dependent on a part of the primary key.

94
Q

Validation

A

Find out which model is the best.

95
Q

Persisted

A

SQL Function

96
Q

SQL View

A

USE Database; go CREATE VIEW schema.viewname AS Select…;

97
Q

ACID

A

Atomicity- Guarantees that each transaction is treated as a single “unit”, which either succeeds completely, or fails completely.Consistency- Ensures that a transaction can only bring the database from one valid state to another (transaction follows constraints, cascades, triggers, etc…)Isolation- Running transactions simultaneously produces the same outcome as running them sequentially.Durability- Once a transaction has been committed, it will remain committed. (Recorded in non-volatile memory).

98
Q

Scoping

A

D

99
Q

Domain Integrity

A

Integrity within column (consistent data type, nullable, add rules(1:10), prior to x date)

100
Q

Referential Enforcement

A

Cascade :Deletes all children when parent is deleted.Set Default: Catch all value when parent gets deleted.set null

101
Q

Surrogate Keys

A

Alternate primary keys that can be used to check the validity of primary key.

102
Q

Why not index everything?

A

Indexes have to be updated every time table changes.

103
Q

Equivocate

A

Use ambiguous language so as to conceal the truth or avoid committing oneself.“Not that we are aware of,” she equivocated”

104
Q

Not

A

Separate

105
Q

Variable scope, block scope

A

D

106
Q

4? Byte

A

+- 2,000,000,000 (int) (32 bit)

107
Q

Insert Into With Select

A

Insert IntoSelect * FROMWHERE

108
Q

Gradient

A

Slope

109
Q

Dot Product

A

Inner Product

Single value.

110
Q

Orthogonal

A

Perpendicular

dot product = 0

111
Q

Staging Tables

A

Raw data is put into table, then processed from that table. Faster than directly processing data source.

112
Q

Measure

A

Measures provide information on a given process such as the process of selling something. For example, the measured value 15 may represent a number of items sold.

113
Q

Fact Table

A

Most of the time, Fact tables are design around an event that happened within your company, for example the event of a product being sold.

114
Q

Dimension Table

A

When talking about a data warehouse dimension we are describing different aspects of the measured event; what product was sold? What customer bought it, what date did it happen, and on what sales order and line item did it occur.

(Data)

115
Q

IDE

A

Integrated Development Environment

Includes editor, debugger, automation.

VSStudio, VSCode etc…

Prog

116
Q

Insert

A

array_name.insert(loc,value)

prog

117
Q

Append

A

array_name.append()

118
Q

gini attribute

A

measures its impurity:
a node is “pure” (gini=0) if all training instances it applies to belong to the same
class.

Green Node

119
Q

feature

A
120
Q

Cart Training Algorithm

A

data

121
Q
A
122
Q
A
123
Q
A

General (5 decks)

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