Top IT Certification Exams In 2008, mcse 2008, mcitp, mcts at certkingdom.com: 02/01/2020

February 23, 2020

5V0-62.19 VMware Workspace ONE Design and Advanced Integration Specialist Exam

Exam Details
The VMware Workspace ONE Design and Advanced Integration Specialist 2019 badge holder is a technical expert that understands how to design, deploy and support the ongoing management of a Workspace ONE app catalog. Achieving this badge validates a deep understanding of how to integrate and configure an identity provider, define the correct application access rights with the help of access policies, and how to achieve single sign-on and multi-factor authentication.

Exam Delivery
This is a non-proctored exam delivered through Pearson VUE. For more information, visit the Pearson VUE website.

Badge Information
For details and a complete list of requirements and recommendations for attainment, please reference the VMware Education Services – Certification website.

Minimally Qualified Candidate
The minimally qualified candidate should be familiar with designing, deploying, and supporting a Workspace ONE app catalog. They should understand how to integrate and configure an identity provider. They should be familiar with single sign-on and multi-factor authentication. Candidates should have completed all recommended training courses for this credential. The successful candidate will likely hold additional industry-recognized IT certifications or accreditations. The MQC should have all the knowledge contained in the exam sections listed below.

Exam Sections
Section 1- Introduction
Section 2- Authentication Method Deep-dive
Section 3- Mobile SSO
Objective 3.1 Outline the work flow of iOS and Android Mobile SSO
Objective 3.2 Configure Mobile SSO for iOS native applications
Objective 3.3 Configure Mobile SSO for Android native applications

Section 4- Single Sign-On for Application
Objective 4.1 Configure web application SSO with SAML protocol
Objective 4.2 Configure web application SSO with OAuth 2.0 protocol
Section 5- Azure AD and Office 365 Integration
Objective 5.1 Given a use case Azure AD with Workspace ONE, identify the implementation flow that should be used.
Objective 5.2 Outline the steps of configuring Office 365 SSO with VMware Identity Manager
Objective 5.3 Outline the steps of integrating Azure AD as a 3rd party identity provider for Workspace ONE

Section 6- Integration with Okta
Objective 6.1 Outline the workflow of Okta integration
Objective 6.2 Outline the steps of integrating Okta as the identity provider
Objective 6.3 Outline the steps of integrating Okta as the service provider
6.4 Objective: Add Okta applications to Workspace ONE.

Section 7- Additional 3rd Party IDP Integration
Objective 7.1 Outline the steps of integrating with AD FS
Objective 7.2 Outline the steps of integrating with PING
Objective 7.3 Configure a 3rd party identity provider in the VMware Identity Manager console
Objective 7.4 Configure authentication methods for the integrated 3rd party identity providers
Objective 7.5 Modify access policies to accommodate the integrated 3rd party identity providers

Section 8- Advanced Configurations
Objective 8.1 Configure the steps of the workflow of UAG identity bridging
Objective 8.2 Outline the steps of incorporating IP restrictions into Conditional Access
Objective 8.3 Outline the high-level steps of configuring KCD with SEG

Section 9- Workspace ONE AirLift
Objective 9.1 Outline the steps of Workspace ONE AirLift deployment
Objective 9.2 Configure environment settings in the Workspace ONE AirLift management interface
Objective 9.3 Migrate collections and applications from SCCM to Workspace ONE UEM
Objective 9.4 Enroll Windows 10 endpoints into co-management

Section 10- 10 Integrating with Horizon
Objective 10.1 Analyze/Apply (Scenario) Configure VIDM connector for Horizon.

Section 11- Workspace ONE Plan and Design Course Introduction

Section 12- Workspace ONE Plan and Design Fundamentals

Section 13- Identifying Use Cases
Objective 13.1 Identify the key Workspace ONE UEM use cases
Objective 13.2 Identify the key VMware Identity Manager use cases
Objective 13.3 Identify the key Horizon use cases
Objective 13.4 Collect use cases and user experience requirement for VMware solution design
Objective 13.5 Identify design considerations when joint VMware solutions are integrated, (for example: WorkSpace ONE, UEM, and VMware Horizons.)

Section 14- Creating Logical and Physical Designs
Objective 14.1 Design the high-level logical solution architecture
Objective 14.2 Analyze/Apply (Scenario) Validate the logical solution architecture
Objective 14.3 Analyze/Apply (Scenario) Create the physical solution architecture
Objective 14.4 Analyze/Apply (Scenario) Validate the physical solution architecture
Objective 14.5 Analyze/Apply (Scenario) Design appropriate Workspace ONE solution roll-out plan for end users

Section 15- Workspace ONE Solution Delivery

Recommended Course
VMware Workspace ONE Design and Advanced Integration Training course

QUESTION 1
The latest Configuration Service Provider (CSP) release by Microsoft might not always be visually available in Workspace ONE UEM to configure.
What should be used to create custom settings to distribute through Workspace ONE UEM if that is true?

A. Download the add-on from my.workspaceone.com.
B. Click the Update button in the Custom Settings profile.
C. Use the Device Description Framework.
D. Export them from GPO.

Answer: C

QUESTION 2
An administrator configured a Service Provider app to authenticate through SAML to the Service Provider from
VMware Identity Manager (vIDM).
Where is the signing certificate?

A. vIDM admin console: Catalog/WebApps/Settings/SaasApps/SAML Metadata
B. vIDM app console: Identity and Access Management/Settings/WebApps//SaasApps/SAML Metadata
C. vIDM app console: Catalog/WebApps/Settings/SaasApps/SAML Metadata
D. vIDM admin console: Identity and Access Management/Settings/WebApps//SaasApps/SAML Metadata

Answer: C

QUESTION 3
Which tasks need to be completed before a third-party identity provider instance can be added in Workspace ONE?

A. Configure the Metadata on the third-party side to match Workspace ONE.
B. Verify that the third-party instances is SAML 1.0 compliant.
C. VMware Identity Manager service must reach the third-party instance.
D. Verify that the third-party instances is REST compliant.

Answer: C

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5V0-62.19 VMware Workspace ONE Design and Advanced Integration Specialist Exam

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February 6, 2020

PSM-I Professional Scrum Master I Exam

Prove Your Scrum Knowledge and Get Certified
The Professional Scrum MasterTM level I (PSM I) assessment is available to anyone who wishes to validate his or her depth of knowledge of the PictureScrum framework and its application. Those who pass the assessment will receive the industry recognized PSM I Certification to demonstrate their fundamental level of Scrum mastery.

Taking a course is not required and if you feel that you already possess a high level of Scrum knowledge, understanding of the Scrum Guide and how to apply Scrum within Scrum Teams, you have the option to take the PSM I assessment directly. However, there are courses available to help you prepare for the assessment and gain a stronger understanding of Scrum. Depending on your role, either the 2-day Professional Scrum Foundations or Professional Scrum Master course is highly recommended.

Prepare for the Assessment
The assessment is difficult; preparation is required to achieve a passing score. Reading the Scrum Guide and taking the Open Assessments alone isn't typically enough preparation and we recommend reviewing the content below:

The assessment covers topics from multiple Focus Areas defined by the Professional Scrum Competencies.
Many questions ask you to think about or interpret the meaning from the Scrum Guide and how you would apply Scrum within a Scrum Team.
The PSM I Suggested Reading lists the Focus Areas from which questions may be selected during the assessment.
The Scrum Master Learning Path contains many additional resources to help you prepare to take the PSM I Assessment.

Search the list of existing Professional Scrum certification holders here or see a breakdown on how many people hold certifications.

To protect the integrity of the certification process, Scrum.org does not share information about the specific questions that were answered correctly or incorrectly. Learn More

The cost of PSM I is $150 USD. Assessment passwords are valid for one attempt, do not expire and remain valid until used. See below for more details.

Details
Fee: $150 USD per attempt
Passing score: 85%
Time limit: 60 minutes
Number of Questions: 80
Format: Multiple Choice, Multiple Answer and True/False
Difficulty: Intermediate
Language: English only
Scrum Resources
Required course: None
Recommended courses: Professional Scrum Foundations or Professional Scrum Master
PSM Subject Areas
Practice Assessment: Scrum Open
Ways to Learn More to help you prepare
Passwords have no expiration date, but are valid for one attempt only

When you purchase a password, it is set up in our system and emailed to you within one business day. All Students in a PSM or PSF course are emailed a password upon completion of the course (typically within 3-5 business days).**

Certification
If you pass the PSM I assessment you will receive the industry-recognized "PSM I" certification, along with a PSM I logo that you can use to identify your achievement. In addition, your name will be posted publicly for colleagues, managers, and potential employers to see.

Unlike other Scrum certifications that require only class attendance, Scrum.org certification requires a minimum score on an online assessment. Attending a course is neither required nor sufficient for certification. This gives Scrum.org certification teeth and ensures that it has true value in the marketplace.

Search the list of Professional Scrum Certificate Holders

QUESTION 1
When many Scrum Teams are working on the same product, should all of their increments be integrated every Sprint?

A. Yes, but only for Scrum Teams whose work has dependencies.
B. Yes, otherwise the Product Owners (and stakeholders) may not be able to accurately inspect what is done.
C. No, each Scrum Team stands alone.
D. No, that is far too hard and must be done in a hardening Sprint.

Correct Answer: B

QUESTION 2
When can a Development Team cancel a Sprint?

A. It can’t. Only Product Owners can cancel Sprints.
B. When functional expectations are not well understood.
C. When the Product Owner is absent too often.
D. When the selected Product Backlog items for the Sprint become unachievable.
E. When a technical dependency cannot be resolved.

Correct Answer: A

QUESTION 3
Which output from Sprint Planning provides the Development Team with a target and overarching direction for the Sprint?

A. The Sprint Backlog.
B. The Sprint Goal
C. The release plan.
D. Sprint Review minutes.

Correct Answer: B

QUESTION 4
How should a Development Team deal with non-functional requirements?

A. Ensure every Increment meets them.
B. Make sure the release department understands these requirements, but it is not the Development Team’s responsibility.
C. Handle them during the Integration Sprint preceding the Release Sprint.
D. Assign them to the lead developers on the team.

Correct Answer: A

QUESTION 5
When is a Sprint over?

A. When the Product Owner says it is done.
B. When all Product Backlog items meet their definition of “Done”.
C. When all the tasks are completed.
D. When the time-box expires.

Correct Answer: D

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February 5, 2020

A00-240 SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Exam

Regression and Modeling
Designed for SAS professionals who use SAS/STAT® software to conduct and interpret complex statistical data analysis
Successful candidates should have experience in:
Analysis of variance.
Linear and logistic regression.
Preparing inputs for predictive models.
Measuring model performance.

ANOVA - 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors

Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures

Prepare Inputs for Predictive Model Performance - 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring

ANOVA - 10%
Verify the assumptions of ANOVA
 Explain the central limit theorem and when it must be applied
 Examine the distribution of continuous variables (histogram, box-whisker, Q-Q plots)
 Describe the effect of skewness on the normal distribution
 Define H0, H1, Type I/II error, statistical power, p-value
 Describe the effect of sample size on p-value and power
 Interpret the results of hypothesis testing
 Interpret histograms and normal probability charts
 Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
 Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
 For a given experiment, verify that the observations are independent
 For a given experiment, verify the errors are normally distributed
 Use the UNIVARIATE procedure to examine residuals
 For a given experiment, verify all groups have equal response variance
 Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance

Analyze differences between population means using the GLM and TTEST procedures
 Use the GLM Procedure to perform ANOVA
o CLASS statement
o MODEL statement
o MEANS statement
o OUTPUT statement

 Evaluate the null hypothesis using the output of the GLM procedure
 Interpret the statistical output of the GLM procedure (variance derived from MSE, F value, p-value R**2, Levene's test)
 Interpret the graphical output of the GLM procedure
 Use the TTEST Procedure to compare means

Perform ANOVA post hoc test to evaluate treatment effect
 Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
 Use PDIFF option of LSMEANS statement
 Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
 Interpret diffograms to evaluate pairwise comparisons
 Interpret control plots to evaluate pairwise comparisons
 Compare/Contrast use of pairwise T-Tests, Tukey and Dunnett comparison methods

Detect and analyze interactions between factors
 Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
 LSMEANS with SLICE=option (Also using PROC PLM)
 ODS SELECT
 Interpret the output of the GLM procedure to identify interaction between factors: p-value
 F Value
 R Squared
 TYPE I SS
 TYPE III SS

Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
 Use the REG procedure to fit a multiple linear regression model
 Use the GLM procedure to fit a multiple linear regression model

Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
 Interpret REG or GLM procedure output for a multiple linear regression model: convert models to algebraic expressions
 Convert models to algebraic expressions
 Identify missing degrees of freedom
 Identify variance due to model/error, and total variance
 Calculate a missing F value
 Identify variable with largest impact to model
 For output from two models, identify which model is better
 Identify how much of the variation in the dependent variable is explained by the model
 Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)

Use the REG or GLMSELECT procedure to perform model selection
 Use the SELECTION option of the model statement in the GLMSELECT procedure
 Compare the different model selection methods (STEPWISE, FORWARD, BACKWARD)
 Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
 Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
 Assign names to models in the REG procedure (multiple model statements)

Assess the validity of a given regression model through the use of diagnostic and residual analysis
 Explain the assumptions for linear regression
 From a set of residuals plots, asses which assumption about the error terms has been violated
 Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
 Explain options for handling influential observations
 Identify collinearity problems by examining REG procedure output
 Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
 Identify experiments that require analysis via logistic regression
 Identify logistic regression assumptions
 logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
 Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)

Optimize model performance through input selection
 Use the LOGISTIC procedure to fit a multiple logistic regression model
 LOGISTIC procedure SELECTION=SCORE option
 Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure

Interpret the output of the LOGISTIC procedure
 Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
 Testing Global Null Hypothesis table
 Type 3 Analysis of Effects table
 Analysis of Maximum Likelihood Estimates table
 Association of Predicted Probabilities and Observed Responses

Score new data sets using the LOGISTIC and PLM procedures
 Use the SCORE statement in the PLM procedure to score new cases
 Use the CODE statement in PROC LOGISTIC to score new data
 Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
 Use the INMODEL/OUTMODEL options in PROC LOGISTIC
 Explain how to score new data when you have developed a model from a biased sample

Prepare Inputs for Predictive Model Performance - 20%

Identify the potential challenges when preparing input data for a model
 Identify problems that missing values can cause in creating predictive models and scoring new data sets
 Identify limitations of Complete Case Analysis
 Explain problems caused by categorical variables with numerous levels
 Discuss the problem of redundant variables
 Discuss the problem of irrelevant and redundant variables
 Discuss the non-linearities and the problems they create in predictive models
 Discuss outliers and the problems they create in predictive models
 Describe quasi-complete separation
 Discuss the effect of interactions
 Determine when it is necessary to oversample data

Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
 Use ARRAYs to create missing indicators
 Use ARRAYS, LOOP, IF, and explicit OUTPUT statements

Improve the predictive power of categorical inputs
 Reduce the number of levels of a categorical variable
 Explain thresholding
 Explain Greenacre's method
 Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure

o METHOD=WARD option
o FREQ, VAR, ID statement
o Use of ODS output to create an output data set

 Convert categorical variables to continuous using smooth weight of evidence

Screen variables for irrelevance and non-linear association using the CORR procedure
 Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
 Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
 Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations

Screen variables for non-linearity using empirical logit plots
 Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
 Interpret RANK procedure output
 Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
 Create empirical logit plots with the SGPLOT procedure
 Interpret empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
 Explain techniques to honestly assess classifier performance
 Explain overfitting
 Explain differences between validation and test data
 Identify the impact of performing data preparation before data is split

Assess classifier performance using the confusion matrix
 Explain the confusion matrix
 Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
 Explain the effect of oversampling on the confusion matrix
 Adjust the confusion matrix for oversampling

Model selection and validation using training and validation data
 Divide data into training and validation data sets using the SURVEYSELECT procedure
 Discuss the subset selection methods available in PROC LOGISTIC
 Discuss methods to determine interactions (forward selection, with bar and @ notation)
 Create interaction plot with the results from PROC LOGISTIC
 Select the model with fit statistics (BIC, AIC, KS, Brier score)

Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
 Explain and interpret charts (ROC, Lift, Gains)
 Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
 Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
 Explain the concept of depth as it relates to the gains chart

Establish effective decision cut-off values for scoring
 Illustrate a decision rule that maximizes the expected profit
 Explain the profit matrix and how to use it to estimate the profit per scored customer
 Calculate decision cutoffs using Bayes rule, given a profit matrix
 Determine optimum cutoff values from profit plots
 Given a profit matrix, and model results, determine the model with the highest average profit

Note: All 22 main objectives will be tested on every exam. The 126 expanded objectives are provided for additional explanation and define the entire domain that could be tested.

QUESTION 1
When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?

A. The sample means from the validation data set are applied to the training and test data sets.
B. The sample means from the training data set are applied to the validation and test data sets.
C. The sample means from the test data set are applied to the training and validation data sets.
D. The sample means from each partition of the data are applied to their own partition.

Correct Answer: B

QUESTION 2
An analyst generates a model using the LOGISTIC procedure. They are now interested in getting the
sensitivity and specificity statistics on a validation data set for a variety of cutoff values.
Which statement and option combination will generate these statistics?

A. Score data=valid1 out=roc;
B. Score data=valid1 outroc=roc;
C. mode1 resp(event= '1') = gender region/outroc=roc;
D. mode1 resp(event"1") = gender region/ out=roc;

Correct Answer: B

QUESTION 3
In partitioning data for model assessment, which sampling methods are acceptable? (Choose two.)
A. Simple random sampling without replacement
B. Simple random sampling with replacement
C. Stratified random sampling without replacement
D. Sequential random sampling with replacement
Correct Answer: AC

QUESTION 4
An analyst has a sufficient volume of data to perform a 3-way partition of the data into training, validation, and test sets to perform honest assessment during the model building process.
What is the purpose of the training data set?

A. To provide an unbiased measure of assessment for the final model.
B. To compare models and select and fine-tune the final model.
C. To reduce total sample size to make computations more efficient.
D. To build the predictive models.

Correct Answer: A

QUESTION 5
The total modeling data has been split into training, validation, and test data. What is the best data to use for model assessment?

A. Training data
B. Total data
C. Test data
D. Validation data

Correct Answer: D
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