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 the algorithms used in QualityStage to improve data quality
- Understand general concept of data quality and QualityStage
- Understand QualityStage commonly used components and processes
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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(b.“LASTNAME”)) AND b.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”
Which of the following is TRUE regarding the use of Critical Fields?
- Critical Fields do not have to match exactly.
- 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.
In Standardization Rule Sets, classification table contains what?
- Field name, data type, and position in record.
- Token, standard form, class, and override rules.
- Class, token, and standard form.
- 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?
- Exporting log messages for a job.
- Setting up project properties.
- Compiling all the jobs at once.
- Scheduling jobs to execute.
What does character discrete investigation examine?
- Single domain fields.
- None of the above.
- Multiple domain fields.
- 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.
- Enabling run time column propagation for parallel jobs.
- Compiling and executing a job.
Which one of the following is NOT a survive technique?
- Most Recent
- Most Frequent
- Longest Field
- Probability based rule
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.
- Use only one Match Pass.
- Add additional passes.
A QualityStage investigate job will NOT ________________
- group records together that are considered duplicates.
- discover trends and anomalies.
- examine single domain and free-form text fields.
- identify invalid and default values.
What does the informational content of a QualityStage Match phase measure?
- The significance of discriminating value and frequency.
- The confidence of a match.
- The degree of importance of a particular data field.
- The severity of false positives and false negatives.
What does the QualityStage Standardization Rule Set contain?
- Logic for parsing, classifying, and pattern processing the data.
- Information on how to perform calculation with the data.
- Rules for eliminating columns of data.
- Rules on which records to keep
Which one of the following is NOT a necessary step in QualityStage to address data quality issues?
- Survive
- Investigate
- Standardize
- Transform
If data value AEJPA 5342G is investigated using an all-C mask, what would the result look like?
- CCCCBCCCCC
- AEJPA 5342G
- AEJPA XXXXG
- AEJPA G
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.
- Reference Match has many-to-one correspondence.
- All of the above.
What will a QualityStage match job do?
- Enforce business standards on data elements.
- Gain complete understanding of data within context.
- Create cross reference keys.
- Construct consolidated household views of individuals.
Which of the following is Weight Override used to add or replace?
- Disagreement weight
- Agreement weight
- Calculated weight
- All of the above.
Which of the following is NOT a Match Stage comparison method?
- CNT_DIFF
- FREQUENCY
- UNCERT
- NAME_UNCERT
Which of the following will be generated by a QualityStage Word Investigate job?
- Pattern report and token report.
- Sample report and detail report.
- Pattern report and output report.
- Sample report and token report.
What is Character Concatenate method used for?
- To investigate one or more fields to uncover any relationship between the field values.
- To analyze data using one or more character masks.
- To generate reports for frequency and pattern references.
- All of the above.
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IBM P2090-095 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Standardization | 20% | - Rule sets and classification - Parsing and formatting data - Standardization concepts and workflow |
| Survivorship and Job Execution | 15% | - Outputs and reporting - Survive stage and rules - Building and running QualityStage jobs |
| Data Matching and Comparison | 25% | - Match stage concepts - Agreement and disagreement weights - Comparison methods and weighting |
| Data Profiling and Investigation | 20% | - Tokenization and pattern analysis - Investigation stages and techniques - Interpreting profiling results |
| Introduction to InfoSphere QualityStage | 20% | - Architecture and components - Data quality concepts and lifecycle - Purpose and core capabilities |

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