Get Quality Preparation with SAS Big Data Professional (A00-220) Certification
- Palak Mazumdar
- Dec 1, 2021
- 9 min read
SAS Big Data Professional certification questions and exam summary helps you to get focused on the exam. This guide also helps you to be on A00-220 exam track to get certified with good score in the final exam.
SAS Big Data Professional (A00-220) Certification Summary
● Exam Name: SAS Big Data Preparation, Statistics, and Visual Exploration
● Exam Code: A00-220
● Exam Duration: 110 minutes
● Exam Questions: 55 to 60 Multiple choice questions
● Passing Score: 67%
● Exam Price: $180 (USD)
● Training
● Exam Registration: Pearson VUE
● Sample Questions: SAS Big Data Professional Certification Sample Question
● Practice Exam: SAS Big Data Professional Certification Practice Exam
SAS Big Data Professional (A00-220) Certification Exam Syllabus
01. Data Management - 50%
Navigate within the Data Management Studio Interface
- Register a new QKB
- Create and connect to a repository
- Define a data connection
- Specify Data Management Studio options
- Access the QKB
- Create a name value macro pair
- Access the business rules manager
- Access the appropriate monitoring report
- Attach and detach primary tabs
Create, design and be able to explore data explorations and interpret results
Define and create data collections from exploration results
Create and explore a data profile
- Create a data profile from different sources (text file, filtered table, SQL query)
- Interpret results (frequency distribution & pattern)
- Use collections from profile results
Design data standardization schemes
- Build a scheme from profile results
- Build a scheme manually
- Update existing schemes
Create Data Jobs
- Rename output fields
- Add nodes and preview nodes
- Run a data job
- View a log and settings
- Work with data job settings and data job displays
- Best practices (how do you ensure that you are following a particular best practice): examples: insert notes, establish naming conventions
- Work with branching
- Join tables
- Apply the Field layout node to control field order
- Work with the Data Validation node:
- Add it to the job flow
- Specify properties/review properties
- Edit settings for the Data Validation node
- Work with data inputs
- Work with data outputs
- Profile data from within data jobs
- Interact with the Repository from within Data Jobs
- Determine how data is processed
- Data job variables
- Set Sorting properties for the Data Sorting node
- Set appropriate advanced properties options for the Data Sorting Node
Apply a Standardization definition and scheme
- Use a definition
- Use a scheme
- Be able to determine the differences between definition and scheme
- Explain what happens when you use both a definition and scheme
- Review and interpret standardization results
- Be able to explain the different steps involved in the process of standardization
Apply Parsing definitions
- Distinguish between different data types and their tokens
- Review and interpret parsing results
- Be able to explain the different steps involved in the process of parsing
- Use parsing definition
Compare and contrast the differences between identification analysis and right fielding nodes
- Review results
- Explain the technique used for identification (process of the definition)
Apply the Gender Analysis node to determine gender
- Use gender definition
- Interpret results
- Explain different techniques for accomplishing gender analysis
Create an Entity Resolution Job
- Use a node in the data job that is the clustering node and explain why you would want to use it
- Survivorship (surviving record identification)
- Record rules
- Field rules
- Options for survivorship
- Discuss and apply the Cluster Diff node
- Apply Cross-field matching (new option)
- Use the Match Codes Node to select match definitions for selected fields
- Outline the various uses for match codes (join)
- Use the definition
- Interpret the results
- Match versus match parsed
- Explain the process for creating a match code
- Select sensitivity for a selected match definition
- Apply matching best practices
Define and create business rules
- Use Business Rules Manager
- Create a new business rule
- Name/label rule
- Specify type of rule
- Define checks
- Specify fields
- Distinguish between different types of business rules
- Row
- Set
- Group
- Apply business rules
- Profile
- Execute business rule node
- Use of Expression Builder
- Apply best practices
Describe the organization, structure and basic navigation of the QKB
- Identify and describe locale levels (global, language, country)
- Navigate the QKB (tab structure, copy definitions, etc.)
- Identify data types and tokens
Be able to articulate when to use the various components of the QKB
- Components include:
- Regular expressions
- Schemes
- Phonetics library
- Vocabularies
- Grammar
- Chop Tables
Define the processing steps and components used in the different definition types
- Identify/describe the different definition types
- Parsing
- Standardization
- Match
- Identification
- Casing
- Extraction
- Locale guess
- Gender
- Patterns
02. ANOVA and Regression - 30%
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
- CLASS statement
- MODEL statement
- MEANS statement
- 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 affect
- 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
- PLM
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
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 colinearity problems by examining REG procedure output
- Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)
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
- LOGISCTIC 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
03. Visual Data Exploration - 20%
Examine, modify, and create data items
- Create and use parameterized data items
- Examine data item properties and measure details
- Change data item properties
- Create custom sorts
- Create distinct counts
- Create aggregated measures
- Create calculated items
- Create hierarchies
- Create custom categories
Select and work with data sources
- Work with multiple data sources
- Change data sources
- Refresh data sources
Create, modify, and interpret automatic chart visualizations in Visual Analytics Explorer
- Identify default visualizations
- Identify the properties available in an automatic chart
Create, modify, and interpret graph and table visualizations in Visual Analytics Explorer
- Work with list table visualizations
- Work with crosstab visualizations
- Work with bar chart visualizations
- Work with line chart visualizations
- Work with scatter plot visualizations
- Work with bubble plot visualizations
- Work with histogram visualizations
- Work with box plot visualizations
- Work with heat map visualizations
- Work with geo map visualizations
- Work with treemap visualizations
- Work with correlation matrix visualizations
Enhance visualizations with analytics within Visual Analytics Explorer
- Add fit lines to visualizations
- Create forecasts
- Interpret word clouds
Interact with visualizations and explorations within Visual Analytics Explorer
- Control appearance of visualizations within explorations
- Add comments to visualizations and explorations
- Use filters on data source and visualizations
- Share explorations
- Share visualizations
SAS Big Data Professional (A00-220) Certification Questions
01. How are the Field name analysis and Sample data analysis methods similar?
a) They both utilize a match definition from the Quality Knowledge Base.
b) They both require the same identification analysis definition from the Quality Knowledge Base.
c) They both utilize an identification analysis definition from the Quality Knowledge Base.
d) They both require the same match definition from the Quality Knowledge Base.
02. Using SAS Visual Analytics Explorer, a content developer would like to examine the relationship between two measures with high cardinality.
Which visualization should the developer use?
a) Scatter Plot
b) Heat Map
c) Scatter Plot Matrix
d) Treemap
03. In SAS Visual Analytics Explorer, when a date data item is dragged onto an Automatic Chart visualization either a bar chart or a line chart will be created.
What determines the type of chart created?
a) The format applied to the date data item determines the type of chart displayed.
b) A bar chart is created if the Model property of the data item is set to Discrete, and a line chart is created if the Model property is set to Continuous.
c) The properties associated with the automatic chart determines the type of chart displayed.
d) A line chart is created if the Model property of the data item is set to Discrete, a bar chart is created if the Model property is set to Continuous.
04. Which option in the properties of a Clustering node allows you to identify which clustering condition was satisfied?
a) Condition matched field prefix
b) Cluster condition field matched
c) Cluster condition field count
d) Cluster condition met field
05. A financial analyst wants to know whether assets in portfolio A are more risky (have higher variance) than those in portfolio B.
The analyst computes the annual returns (or percent changes) for assets within each of the two groups and obtains the following output from the GLM procedure:
Which conclusion is supported by the output?
a) Assets in portfolio A are significantly more risky than assets in portfolio B.
b) Assets in portfolio B are significantly more risky than assets in portfolio A.
c) The portfolios differ significantly with respect to risk.
d) The portfolios do not differ significantly with respect to risk.
06. A Data Quality Steward creates these items for the Supplier repository:
- A row-based business rule called Monitor for Nulls
- A set-based business rule called Percent of Verified Addresses
- A group-based rule called Low Product Count
- A task based on the row-based, set-based, and group-based rules called Monitor Supplier Data
Which one of these can the Data Quality Steward apply in an Execute Business Rule node in a data job?
a) set-based business rule called Percent of Verified Addresses
b) row-based business rule called Monitor for Nulls
c) group-based rule called Low Product Count
d) task based on the row-based, set-based, and group-based rules called Monitor Supplier Data
07. How do you access the Data Management Studio Options window?
a) from the Tools menu
b) from the Administration riser bar
c) from the Information riser bar
d) in the app.cfg file in the DataFlux Data Management Studio installation folder
08. When selecting variables or effects using SELECTION=BACKWARD in the LOGISTIC procedure, the business analyst's model selection terminated at Step 3.
What happened between Step 1 and Step 2?
a) DF increased.
b) AIC increased.
c) Pr > Chisq increased.
d) - 2 Log L increased.
09. A sample of data has been clustered and found to contain many multi-row clusters. To construct a "best" record for each multi-row cluster, you need to select information from other records within a cluster.
Which type of rule allows you to perform this task?
a) Clustering rules
b) Record rules
c) Business rules
d) Field rules
10. A linear model has the following characteristics:
- a dependent variable (y)
- one continuous predictor variables (x1) including a quadratic term (x12)
- one categorical predictor variable (c1 with 3 levels)
- one interaction term (c1 by x1)
Which SAS program fits this model?
a) proc glm data=SASUSER.MLR;
class c1;
model y = c1 x1 x1sq c1byx1 /solution;
run;
b) proc reg data=SASUSER.MLR;
model y = c1 x1 x1sq c1byx1 /solution;
run;
c) proc glm data=SASUSER.MLR;
class c1;
model y = c1 x1 x1*x1 c1*x1 /solution;
run;
d) proc reg data=SASUSER.MLR;
model y = c1 x1 x1*x1 c1*x1;
run;
Answers:
Question: 01: Answer: c
Question: 02: Answer: b
Question: 03: Answer: b
Question: 04: Answer: a
Question: 05: Answer: c
Question: 06: Answer: b
Question: 07: Answer: a
Question: 08: Answer: d
Question: 09: Answer: d
Question: 10: Answer: c
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