Q::46. Data Mining can be used as ................. Tool.
(A) Software (B) Hardware
(C) Research (D) Process
Answer: C
Explanation:
Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence.
The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.
The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Q::47 The processing speeds of pipeline segments are usually:
(A) Equal (B) Unequal
(C) Greater (D) None of these
Answer: B
Explanation:
Pipelining
In computing, a pipeline is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion; in that case, some amount of buffer storage is often inserted between elements.Computer-related pipelines include:
Instruction pipelines, such as the classic RISC pipeline, which are used in central processing units (CPUs) to allow overlapping execution of multiple instructions with the same circuitry. The circuitry is usually divided up into stages, including instruction decoding, arithmetic, and register fetching stages, wherein each stage processes one instruction at a time.
Graphics pipelines, found in most graphics processing units (GPUs), which consist of multiple arithmetic units, or complete CPUs, that implement the various stages of common rendering operations (perspective projection, window clipping, color and light calculation, rendering, etc.).
Software pipelines, where commands can be written where the output of one operation is automatically fed to the next, following operation. The Unix system call pipe is a classic example of this concept, although other operating systems do support pipes as well.
Example: Pipelining is a natural concept in everyday life, e.g. on an assembly line. Consider the assembly of a car: assume that certain steps in the assembly line are to install the engine, install the hood, and install the wheels (in that order, with arbitrary interstitial steps). A car on the assembly line can have only one of the three steps done at once. After the car has its engine installed, it moves on to having its hood installed, leaving the engine installation facilities available for the next car. The first car then moves on to wheel installation, the second car to hood installation, and a third car begins to have its engine installed. If engine installation takes 20 minutes, hood installation takes 5 minutes, and wheel installation takes 10 minutes, then finishing all three cars when only one car can be assembled at once would take 105 minutes. On the other hand, using the assembly line, the total time to complete all three is 75 minutes. At this point, additional cars will come off the assembly line at 20 minute increments.
A linear pipeline processor is a series of processing stages and memory access.
A non linear pipelining (also called dynamic pipeline) can be configured to perform various functions at different times. In a dynamic pipeline there is also feed forward or feedback connection. Non-linear pipeline also allows very long instruction word.
Q::48 The cost of a parallel processing is primarily determined by:
(A) Time complexity
(B) Switching complexity
(C) Circuit complexity
(D) None of the above
Answer: B
Explanation:
In digital signal processing (DSP), parallel processing is a technique duplicating function units to operate different tasks (signals) simultaneously. Accordingly, we can perform the same processing for different signals on the corresponding duplicated function units. Further, due to the features of parallel processing, the parallel DSP design often contains multiple outputs, resulting in higher throughput than not parallel.
Consider a function unit (F0) and three tasks (T0, T1 and T2). The required time for the function unit F0 to process those tasks is t0,t1 and t2 respectively. Then, if we operate these three tasks in a sequential order, the required time to complete them is t0 + t1 + t2.
However, if we duplicate the function unit to another two copies (F), the aggregate time is reduced to max(t0,t1,t2), which is smaller than in a sequential order.
Q::49 A data warehouse is always ....................
(A) Subject oriented (B) Object oriented
(C) Program oriented (D) Compiler oriented
Answer: A
Explanation:
Explanation:
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise. Examples of reports could range from annual and quarterly comparisons and trends to detailed daily sales analysis.
The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales, etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before it is used in the DW for reporting.
50. The term 'hacker' was originally associated with:
(A) A computer program
(B) Virus
(C) Computer professionals who solved complex computer problems.
(D) All of the above
Answer: C
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