Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Salesforce Einstein Consultant Exam > Einstein Discovery Terms > Flashcards

Einstein Discovery Terms Flashcards

1
Q

Licenses needed to use Einstein Discovery

A

CRM Analytics Plus or Einstein Predictions License

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Permission sets for Einstein Discovery

A

CRM Analytics Plus User

CRM Analytics Plus Admin

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What can the user permission set do?

A

Use einstein Discovery - only view created stories
View Einstein discovery recommendations.

Admin can do everything.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Descriptive

A

What happened to historical data?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Diagnostic

A

Why did it happen?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Comparative

A

What is the difference between subgroups?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Predictive

A

What could happen?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Prescriptive

A

How can I improve the predicted outcome?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

2 story types

A

Insights only

Insights and Predictions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Insights Only story type

A

produces Descriptive insights only

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Insights and Predictions Story Type

A

Created a model and produces all insight types

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Einstein Discovery Use Cases

A

Numerical - numeric outcomes
Binary - two possible results represented as text data (yes/no)
Multiclass - predict outcomes with 3 to 10 possible results, represented as text data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

3 story templates

A

Maximize customer revenue
Maximize win rate
Minimize time to close

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Einstein Discovery AI ethical features

A

Bias Detection - alerts you to bian

Model cards - document the models

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

CRM Analytics Dataset Limits - # Rows

A

Minimum Descriptive Insights: 50
Minimum Predictive Insights: 400

Max: 20M

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

CRM Analytics Dataset Limits - # Columns

A

Min: 3 (1 outcome, 2 dataset columns)
Max: 50

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

story creations per org per day

A

100

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

story creations per org per month

A

500

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

concurrent story creations per org

A

2

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

concurrent queries per user

A

10

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

queries per user per day

A

10,000

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Prediction Limits for Automated Predictions

A

5 sf objects models can be deployed

10 active models deployed to a single prediction

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Number of unique predictions on a single entity with automated prediction fields

A

3

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Number of predictions requested per org per day using automated prediction fields

A

500K

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

Number of records that can be scored per org per day

A

1M

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

Number of active prediction definitions

A

CRM Analytics Plus License: Unlimited

Einstein Prediction License: 10

27
Q

Number of active models deployed to a single prediction definition using manually configured predictions fields.

A

10

28
Q

Number of Einstein Prediction Service API calls per org per day. The Usage Statistics chart displays the cumulative total for this metric.

A

50K

29
Q

Number of Einstein Prediction Service API requests per user per hour.

A

2000

30
Q

Number of concurrent Einstein Prediction Service API requests within an org.

A

Depends on how the model associated with the request was built:

Einstein Discovery-built models: 25
Externally-built models uploaded to Salesforce: 1

31
Q

Multivalue fields in ED

A

Not supported in ED

32
Q

Usage Statistics Stats

A

Number of predictions run today
Number of story versions created today
Number of concurrent stories that can be analyzed
Number of prediction API calls run today
Number of story versions created this month

33
Q

Actionable Variable

A

Explanatory variable people can control.
If variable is designated as actionable, model uses prescriptive analytics to suggest actions user can take to improve predicted outcome

34
Q

Actual Outcome

A

An actual outcome is the real-world value of an observation’s outcome variable after the outcome has occurred. Einstein Discovery calculates model performance by comparing how closely predicted outcomes come to actual outcomes. An actual outcome is sometimes called an observed outcome.

35
Q

Bias

A

Variables are being treated unequally in your model

36
Q

Cardinality

A

Number of distinct values in a category.
ED supports 100 categories per variable.
Null values are put into category called unspecified.
Can consolidate remaining categories (categories with < 25 obs) into ‘other’ category

37
Q

Categorical Variable

A

Represents Qualitative values. Story with binary or multi-class is categorical.

38
Q

Causation

A

Statistical associated between variables.

39
Q

Diagnostic Insights

A

Insights derived from a model. Show ‘why’ it happened. Drill into correlated variables.

40
Q

Disparate Impact

A

Data reflects discriminatory practices towards a patricular demographic

41
Q

Dominant values

A

Data is unbalanced. Most values are in same category.

42
Q

Drift

A

Drift can occur due to changing factors in the data or in your business environment. Drift also results from now-obsolete assumptions built into the story on which the model is based. To remedy a model that has drifted, you can refresh it by adjusting story settings, retraining it on newer data, and redeploying it.

43
Q

Duplicates

A

Two or more explanetory variables are highly correlated (ex: Zipcode and city) ED recommends choosing just one variable to improve results.

44
Q

Explanatory Variable

A

Variable you explore to determine whether and to what degree if can influence the outcome variable.
Also called input variable, feature, predictor, or independent variable

45
Q

Feature Selection

A

Picking the best explanatory variables in a story.Too few features could result in underfitting, too many could result in overfitting.

Select the most influential explanatory variables with no significant llurking variables

46
Q

First Order analysis

A

how one explanetory variable explains variation in the outcome variable. Also called bivariate analysis

47
Q

Generalized Linear Model

A

Regression absed model

48
Q

Goal

A

Specifies the desired outcome for the story. Includes the story’s outcome variable plus your preferred direction for the outcome.

49
Q

Gradient Boosting

A

Decision-Tree based algorithm.

50
Q

Identical Values

A

All values for a variable belong to the same category

51
Q

Improvement

A

Suggested action based on prescriptive analytics that use can take to improve likelihood of desired outcome. Associated with actionable variables.

52
Q

Imputation

A

stat technique for replacing numeric values with valies derived from subset of data.

53
Q

Insight

A

Starting point for you to investiate the relationsihps among story’s explanatory variables and its goal.

54
Q

k-fold Cross-Validation

A

Model validation process in which Einstein Discovery randomly divides all the observations in the Analytics dataset into four separate partitions of equal size. Next, it completes four test passes (folds) in which three of the partitions serve as the training set and one partition serves as the test set. For each fold, Einstein Discovery compiles model metrics, then averages the metrics for all four folds.

55
Q

Leakage

A

Leakage occurs when the data used to train your model includes one or more variables that contain the information that you are trying to predict. This can result in models that are extremely accurate when, in actuality, they are problematic. To remedy data leakage, remove any variables from your model that are causing the leakage.

56
Q

Lurking Variable

A

A lurking variable is an explanatory variable that is missing from your story but which significantly explains variations in the outcome variable.

57
Q

Modeling Algorithm

A

A modeling algorithm is what Einstein Discovery uses to create a model for a story. Einstein Discovery uses one of several algorithms: generalized linear model (GLM) is a regression-based algorithm, while gradient boosting machine (GBM) and XGBoost are decision tree-based machine learning algorithms.

58
Q

Model Manager

A

The Model Manager is the Einstein Discovery tool used to manage predictions and models you have deployed.

59
Q

Model Metrics

A

Model metrics describe the performance of the predictive model associated with your story. It provides metrics (quality indicators, which are sometimes called fit statistics) to show how well the model’s predictions fit the training data in the dataset. For definitions of quality indicators shown in the Model Metrics tabs, see Evaluate Model Quality.

60
Q

Multiclass Classification Use Case

A

The multiclass classification use case addresses business outcome that have between 3 and 10 outcome values, such as five possible service plans or eight possible insurance policies. Multiclass classification is one of the main use cases that Einstein Discovery supports. Compare with Binary Classification.

61
Q

Noise

A

any data that does not meaningfully explain variations in your outcome variable

62
Q

Overfitting

A

In predictive analytics, overfitting occurs when a model performs well in predicting outcomes on the training data in the dataset, but less well when predicting outcomes for other data, such as production data. Using too many explanatory variables can result in an overly complex predictive model that captures the noise in your data. To mitigate overfitting, Einstein Discovery uses ridge regression and regularization

63
Q

Second-Order Analysis

A

In an insight, a second-order analysis examines how the combination of two explanatory variables explains variation in the outcome variable. In second-order analysis, the combined impact of both variables together on the outcome is sometimes called the interaction effect. Second-order analysis is sometimes called multivariate analysis.

Salesforce Einstein Consultant Exam (7 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions