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How to Prepare For IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
Preparation Guide for IBM InfoSphere QualityStage Fundamentals Technical Mastery Test v1
Introduction
This exam validates the technical knowledge required for the purpose of identifying, managing and closing sales opportunities.The test is applicable to sales representatives who demonstrate sales and technical knowledge of the software and targets the technical sales professional who can deliver a comprehensive business solution to customers through solution identification, product differentiation, and competitive positioning.
Passing this test counts towards PartnerWorld competency requirements and helps you attain higher membership levels. This test also entitles you to use the mastery qualification title on your business card. IBM P2090-95 practice exams and IBM P2090-95 practice test have all questions related to this.
Candidate should give this exam who want:
- Understand general concept of data quality and QualityStage
- Understand the algorithms used in QualityStage to improve data quality
- Understand QualityStage commonly used components and processes
Sample Questions
Which of the following rules will retain FIELD1 with the longest last name and TITLE equals to “MR”?
- FIELD1: SIZEOF(TRIM(c.“LASTNAME”))<= SIZEOF(TRIM(b.“LASTNAME”)) AND c.TITLE=“MR”
- FIELD1: SIZEOF(TRIM(c.“LASTNAME”))>= SIZEOF(TRIM(c.“LASTNAME”)) AND c.TITLE=“MR”
- FIELD1: SIZEOF(TRIM(c.“LASTNAME”))>= SIZEOF(TRIM(b.“LASTNAME”)) AND c.TITLE=“MR”
- FIELD1: SIZEOF(TRIM(c.“LASTNAME”))>= SIZEOF(TRIM(b.“LASTNAME”)) AND b.TITLE=“MR”
Which of the following is TRUE regarding the use of Critical Fields?
- Only critical fields of a record are used to calculate the composite weights of the record.
- Critical Fields are fields with missing values.
- Critical Fields are used to identify fields that must agree in order for records to be linked.
- Critical Fields do not have to match exactly.
In Standardization Rule Sets, classification table contains what?
- Token, standard form, class, and override rules.
- Class, token, and standard form.
- Field name, data type, and position in record.
- Pattern of tokens, actions to execute, and the logic.
Investigation of data using QualityStage is for _____________________
- transforming the data from one data type to another.
- verifying the reliability of the data in the fields to be used as matching criteria.
- discovering the actual values of missing data.
- identifying all the data sources for data consolidation.
What is DataStage/Quality Stage Administrator client used for?
- Setting up project properties.
- Scheduling jobs to execute.
- Exporting log messages for a job.
- Compiling all the jobs at once.
What does character discrete investigation examine?
- Multiple domain fields.
- Single domain fields.
- None of the above.
- Both single domain and multiple domain fields.
What is DataStage/QualityStage Director client used for?
- Publishing and managing shared information services.
- Clearing log messages of a job.
- Compiling and executing a job.
- Enabling run time column propagation for parallel jobs.
Which one of the following is NOT a survive technique?
- Probability based rule
- Most Recent
- Longest Field
- Most Frequent
Which of the following should be avoided regarding the use of Match Improvement Strategy?
- Set critical values for important fields.
- Adjust weights using weight overrides.
- Add additional passes.
- Use only one Match Pass.
A QualityStage investigate job will NOT ________________
- examine single domain and free-form text fields.
- discover trends and anomalies.
- identify invalid and default values.
- group records together that are considered duplicates.
What does the informational content of a QualityStage Match phase measure?
- The degree of importance of a particular data field.
- The significance of discriminating value and frequency.
- The severity of false positives and false negatives.
- The confidence of a match.
What does the QualityStage Standardization Rule Set contain?
- Information on how to perform calculation with the data.
- Rules for eliminating columns of data.
- Logic for parsing, classifying, and pattern processing the data.
- Rules on which records to keep
Which one of the following is NOT a necessary step in QualityStage to address data quality issues?
- Investigate
- Survive
- Transform
- Standardize
If data value AEJPA 5342G is investigated using an all-C mask, what would the result look like?
- AEJPA 5342G
- AEJPA G
- CCCCBCCCCC
- AEJPA XXXXG
Which of the following is NOT true regarding the use of Match Type?
- Unduplicate Match requires two source files.
- Reference Match has one-to-one correspondence.
- All of the above.
- Reference Match has many-to-one correspondence.
What will a QualityStage match job do?
- Gain complete understanding of data within context.
- Enforce business standards on data elements.
- Construct consolidated household views of individuals.
- Create cross reference keys.
Which of the following is Weight Override used to add or replace?
- Calculated weight
- All of the above.
- Agreement weight
- Disagreement weight
Which of the following is NOT a Match Stage comparison method?
- FREQUENCY
- NAME_UNCERT
- UNCERT
- CNT_DIFF
Which of the following will be generated by a QualityStage Word Investigate job?
- Pattern report and token report.
- Sample report and token report.
- Pattern report and output report.
- Sample report and detail report.
What is Character Concatenate method used for?
- To generate reports for frequency and pattern references.
- To investigate one or more fields to uncover any relationship between the field values.
- All of the above.
- To analyze data using one or more character masks.
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IBM P2090-095 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: InfoSphere QualityStage Architecture | - Components and workflow
|
| Topic 2: Matching and Linking | - Match design principles
|
| Topic 3: Data Cleansing and Survivorship | - Record consolidation concepts
|
| Topic 4: Data Quality Fundamentals | - Core concepts of data quality
|
| Topic 5: Standardization and Parsing | - Data standardization techniques
|







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