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Data Quality 9.x Developer Specialist exam Dumps

PR000005 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

You may select from one or more response offerings to answer a question.

You will score the question correctly if your response accurately completes the statement or answers the question. Incorrect
distractors are given as possible correct answers so that those without the required skills and experience may wrongly select that choice.

A passing grade of 70% is needed to achieve recognition as an Informatica Certified Specialist (ICS) Data Quality Developer v9.x.

You are given 90 minutes to complete the exam. Test formats used in this exam are:

• Multiple Choice: Select one option that best answers the question or completes the statement

• Multiple Response: Select all that apply to best answer the question or complete the statement

• True/False: After reading the statement or questions select the best answer



Data Quality Developer 9.x, Specialist Certification

About the ICS Data Quality Developer exam and the Skill Set Inventory

This exam measures your competency as a member of a project implementation team. This involves having an in-depth knowledge of
each of the Data Quality processes from Profiling to Standardization, Matching and Consolidation. You need to be able to select and configure the appropriate Data Quality transformation for your requirements and build, debug and execute Data Quality mappings as well as integrate mappings into Power Center.

The skill set inventory is used to guide your preparation before taking the exam. It is an outline of the technical Topics and subject areas that are covered in each exam. The skill set inventory includes test domain weighting, test objectives and topical content. The Topics and concepts are included to clarify the test objectives.

Test takers will be tested on:

• Their ability to navigate through the Developer Tool

• Use of Project Collaboration techniques; shared Tags, Comments, Profiles, reference tables to share information with Analysts

• Using a variety of profiling methods to profile data

• The use of Analyst built profiles to develop mappings

• Build mappings and mapplets to cleanse and standardize data

• Perform Address Validation

• Identifying duplicate records in a dataset

• Automatically and manually consolidating duplicate records to create a master record

• Execution of mappings/mapplets in PowerCenter

• Deployment of Data Quality Mappings in an Excel spreadsheet (connect to Web Services)



Title % of Test

Informatica Overview 10%

Analyst Collaboration 10%

Profiling 15%

Standardization/Mapplets 10%

Address Validation 5%

Matching 10%

Consolidation and the DQA 10%

Integration with PowerCenter 5%

Object Import and Export 5%

DQ for Excel 5%

Parameters 5%

Content 10%



Test Topics

The exam will contain 70 questions comprised of Topics that span across the sections listed below. In order to ensure that you are prepared for the test, review the subtopics with each section.

Informatica Overview

• Describe the Informatica 9 architecture and set up
including the repositories required for installation

• Provide information on the Data Quality process and
dimensions of data quality

Analyst Collaboration

• Use and describe the functionality of the Analyst Tool including Scorecarding, Reference Table Management, Tags, Filters, Profiles and Comments

• Describe how Analysts and Developers can
collaborate on projects

• Describe the benefits of project collaboration to a
team

Profiling

• Perform and interpret column, rule, comparative and
mid-stream profiling

Standardization/Mapplets

• Be aware of where data standardization fits in the
DQ process

• Apply, configure and troubleshoot data
standardization transformations

• Differentiate between the various parsing techniques
available

• Recognize how reference tables are used in the
standardization process

Address Validation

• Verify the importance of Address Validation

• Configure the Address Validation transformation
including explaining each mode that is available and
what custom templates can be used for

• Interpret the AV outputs generated
Matching

• Develop and build match plans to identify duplicate
or related data

• Differentiate between the matching algorithms
available and explain how matching scores are

• Configure Identity Matching option for the match
transformation

• Explain populations, the identity match strategies that
are available and how they are used in Identity
Matching

Consolidation and the DQA

• Explain Automatic and Manual consolidation
techniques and configure the Transformations used
for automatic consolidation

• Use the Exception Transformation to generate and
populate the Bad Records and Duplicate Records
tables

• Troubleshoot any problems that may occur

• Generate a survivor record in the DQA

• Remove duplicates using DQA
Integration with Power Center

• Explain how to integrate IDQ mappings and
mapplets into PowerCenter Workflows including how
content is handled

Object Import and Export

• Define the difference between the Basic and
Advanced Import options available

• Explain Dependency conflict resolution and how it is
handled

• Describe how to Export a Project
DQ for Excel

• Describe the requirements for integration for Excel

• Define the techniques for creating mappings for DQ
for Excel

• Explain the Web service capabilities required for DQ
for Excel

• Provide an explanation on how to use the Informatica
ribbon in Excel

Parameters

• Explain what parameters are and why they are used
including what parameter functions are supported

• Identify which Transformations can use parameters

• Describe the process of exporting mappings with
parameters built in Content

• Explain what Content is, what is contained in the
Core Accelerator and why it is used.

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Informatica
PR000005
Data Quality 9.x Developer Specialist
https://killexams.com/pass4sure/exam-detail/PR000005
QUESTION: 61
When matching, is it always necessary to group data?
A. Only if the customer thinks it's a good idea
B. Only if the quality of the data is poor
C. Not always. If the dataset is small enough, grouping is not required
D. Yes, it's always necessary
Answer: C
QUESTION: 62
What statement best describes IDQ's Web service capabilities?
A. Mappings can only be created as a web service through PC integration
B. DQ has both native web service functionality and allows integration through PC C.
DQ mappings can only be made accessible through native means
D. DQ has not web service support
Answer: C
QUESTION: 63
How much data can DQA handle?
A. 2000 rows and 20 columns
B. Million rows and 100 columns
C. There are no built-in restrictions. Large deployments will require more hardware and
database tuning
D. Maximum 1 million rows and up to 10,000 columns
Answer: B
QUESTION: 64
Which parameter function is supported in IDQ 9.1?
A. Set parameter for RTM Location/ Path
B. Set parameter for RTM Name
C. Ability to parameterise relational and flat-file connections
D. All of the above
Answer: D
QUESTION: 65
A customer has provided a number of delimited flat files that require profiling, but not
filedefinition. What should you do first?
A. Allow the wizard in Developer to automatically select the data types when creating
the data objects.
B. Set all of the data types to text and length longer than those specified. The profiler
will tell you what they should be.
C. Import the fileinto a database table before profiling. D. Change the code page of the
file to UTF-8.
Answer: A
QUESTION: 66
What is required for customers to access and use IDQ Accelerator content?
A. Customers must license each Accelerator pack individually to get and use the
content.
B. No additional steps are required. All Accelerator content is provided with the basic
IDQ license.
C. IDQ customers simply need to request an IPS resource to get and install the
content.
D. Accelerator content is available only with a PowerCenter license.
Answer: B
QUESTION: 67
What is the primary purpose of grouping data?
A. standardize data in preparation for matching
B. normalize data in preparation for matching
C. Boost match performance
D. Boost match results
Answer: C
QUESTION: 68
Which of the following cannot be executed in BOTH the Analyst and the Developer
Tool?
A. A rule created in the Analyst Tool
B. A mapplet created in the Developer Tool and validated as a rule
C. A mapplet created in the Developer Tool and validated as a mapplet
D. A reusable rule created in the Analyst Tool
Answer: B
QUESTION: 69
Which phrase best describes how organizations should view their approach to data
quality?
A. As a one-off project addressing the issues in their data warehouse in batch
B. As an ongoing process addressing issues over time with a combination of fit-for-
purpose approaches
C. As a real-time process addressing issues as they are introduced
D. As a batch process to be implemented to run on a monthly basis
Answer: A
QUESTION: 70
What Data Quality Transformation utilizes Identity populations?
A. Key Generator Transformation
B. Match Transformation
C. Edit Distance Transformation
D. Comparison Transformation
Answer: C
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