Big Data Analytics

Correct Answer: Precision is the ratio of true positive predictions to the total number of positive predictions made by a model, while recall is the ratio of true positive predictions to the total number of actual positive instances in the dataset.

Precision is the ratio of true positive predictions to the total number of positive predictions made by a model, while recall is the ratio of true positive predictions to the total number of actual positive instances in the dataset. High precision means that there are few false positives in the model predictions, while high recall means that the model is able to correctly identify most of the positive instances in the dataset. The ideal scenario would be having both precision and recall to their highest possible values.

Correct Answer: It is a join operation that is performed on the Map phase of a MapReduce job by using the Distributed Cache to load data into memory.

Map-side Join is a join operation that is performed on the Map phase of a MapReduce job by using the Distributed Cache to load data into memory. This can be more efficient than performing the join on the Reduce phase, since it reduces the amount of data that needs to be shuffled across the network. However, Map-side Join is only applicable for certain types of join operations, such as one-to-one and one-to-many joins.

Correct Answer: Batch processing handles large volumes of data at once while stream processing handles data in real-time as it comes in

Batch processing handles large volumes of data at once, typically with no set time limit for processing. Stream processing handles data in real-time as it comes in, with little or no downtime in processing. Batch processing is better suited for handling large amounts of historical data, while stream processing is more suited for handling real-time data streams or event data.