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IBM Certified Data Architect - Big Data Certification Questions and Answers (Dumps and Practice Questions)



Question : The analysis layer reads the data digested by the data massaging and store layer. In some cases, the analysis layer accesses the data directly from the data source.
Designing the analysis layer requires careful forethought and planning. Decisions must be made with regard to how to manage the tasks to

A. Produce the desired analytics
B. Derive insight from the data
C. Find the entities required
D. Locate the data sources that can provide data for these entities
E. Understand what algorithms and tools are required to perform the analytics.
: The analysis layer reads the data digested by the data massaging and store layer. In some cases, the analysis layer accesses the data directly from the data source.
1. A,B,C
2. C,D,E
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4. A.B,C,D
5. A,B,C,D,E

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Explanation: Analysis layer: The analysis layer reads the data digested by the data massaging and store layer. In some cases, the analysis layer accesses the data
directly from the data source. Designing the analysis layer requires careful forethought and planning. Decisions must be made with regard to how to manage the tasks to:
Produce the desired analytics
Derive insight from the data
Find the entities required
Locate the data sources that can provide data for these entities
Understand what algorithms and tools are required to perform the analytics.




Question : Visualization applications, human beings, business processes, or services can be considered under which logical layer of BigData
: Visualization applications, human beings, business processes, or services can be considered under which logical layer of BigData
1. Big data sources

2. Data massaging and store layer

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4. Consumption layer

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Explanation: Consumption layer: This layer consumes the output provided by the analysis layer. The consumers can be visualization applications, human beings, business
processes, or services. It can be challenging to visualize the outcome of the analysis layer. Sometimes it's helpful to look at what competitors in similar markets are doing.





Question : You are working in Arinika INC, now you need to look for all the characteristics of BigData. Which of the following cannot be a characteristics of BigData
: You are working in Arinika INC, now you need to look for all the characteristics of BigData. Which of the following cannot be a characteristics of BigData
1. Data frequency and size

2. Software

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4. Processing methodology

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Explanation: Using big data type to classify big data characteristics
It's helpful to look at the characteristics of the big data along certain lines " for example, how the data is collected, analyzed, and processed. Once the data is classified, it
can be matched with the appropriate big data pattern:
Analysis type " Whether the data is analyzed in real time or batched for later analysis. Give careful consideration to choosing the analysis type, since it affects several other
decisions about products, tools, hardware, data sources, and expected data frequency. A mix of both types may be required by the use case:
Fraud detection; analysis must be done in real time or near real time.
Trend analysis for strategic business decisions; analysis can be in batch mode.
Processing methodology " The type of technique to be applied for processing data (e.g., predictive, analytical, ad-hoc query, and reporting). Business requirements determine the
appropriate processing methodology. A combination of techniques can be used. The choice of processing methodology helps identify the appropriate tools and techniques to be used in
your big data solution.
Data frequency and size " How much data is expected and at what frequency does it arrive. Knowing frequency and size helps determine the storage mechanism, storage format, and
the necessary preprocessing tools. Data frequency and size depend on data sources:
On demand, as with social media data
Continuous feed, real-time (weather data, transactional data)
Time series (time-based data)
Data type " Type of data to be processed " transactional, historical, master data, and others. Knowing the data type helps segregate the data in storage.
Content format " Format of incoming data " structured (RDMBS, for example), unstructured (audio, video, and images, for example), or semi-structured. Format determines how the
incoming data needs to be processed and is key to choosing tools and techniques and defining a solution from a business perspective.
Data source " Sources of data (where the data is generated) " web and social media, machine-generated, human-generated, etc. Identifying all the data sources helps determine
the scope from a business perspective. The figure shows the most widely used data sources.
Data consumers " A list of all of the possible consumers of the processed data:
Business processes
Business users
Enterprise applications
Individual people in various business roles
Part of the process flows
Other data repositories or enterprise applications
Hardware " The type of hardware on which the big data solution will be implemented " commodity hardware or state of the art. Understanding the limitations of hardware helps
inform the choice of big data solution.




Related Questions

Question : You are working with a Credit Card company, and your marketing company has access to monthly expense detail of CC. However, to retain customer and give them better
offer they are also like to tap on users social data, to understand CC holder behavior. How best can they achieve this task?


 : You are working with a Credit Card company, and your marketing company has access to monthly expense detail of CC. However, to retain customer and give them better
1. By loading both social data in the current Enterprise Data Warehouse, then run analytics.

2. By loading social data in BigInsights for exploration then moving resulting data to Enterprise Data Warehouse, and merging with expense data for analytics

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4. By creating a dedicated data mart in their current Enterprise Data Warehouse



Question : Which of the following are example of unstructured data?
A. HBase table
B. Tweet
C. Netezza table
D. Internet Protocol Detail Record
 : Which of the following are example of unstructured data?
1. A,B,C
2. B,C,D
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4. A,B,D
5. A,B,C,D


Question : Which of the following is a key consideration when determining the potential use of Models in Big Data Deployments?
 : Which of the following is a key consideration when determining the potential use of Models in Big Data Deployments?
1. Stability of schema

2. Level of Information Processing

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4. All of the above


Question : Which of the following statement is true, with regards to BigInsight
 : Which of the following statement is true, with regards to BigInsight
1. Replaces the traditional Data warehouses

2. Can exchange information with the traditional Data warehouses only

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4. Supports data exchange with a number of sources


Question : You are , working as a BigData Project Manager, you have following thins, which needs to be covered

1. Project Requirement for Software Selection
2. Evaluate the initial functional fit of a vendors software solution

Which of the one, will you choose from below ?


 : You are , working as a BigData Project Manager, you have following thins, which needs to be covered
1. Component Model

2. Requirements Matrix

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4. Architecture Diagram


Question : Which of the following is NOT a valid Big Data platform integration?
 : Which of the following is NOT a valid Big Data platform integration?
1. Platform plugins

2. Intraplatform integration

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4. Network integration