Big Data Analytics test - Big Data Scientist questions

Question 1

In machine learning, what is the difference between precision and recall?

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 measures the model's ability to predict positive instances while recall measures its ability to predict negative instances. Precision is the ratio of true positive predictions to the total number of actual positive instances in the dataset, while recall is the ratio of true positive predictions to the total number of positive predictions made by a model. Precision measures the model's ability to predict negative instances while recall measures its ability to predict positive instances. Precision and recall are the exact same measure. Precision is a measure of the model's accuracy while recall is a measure of the model's bias.

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.

Question 2

What is Map-side Join in Hadoop?

It is a configuration parameter used to specify the number of Map tasks to be used in a MapReduce job. It is a technique to compress data in a MapReduce job in order to reduce the amount of data transferred across the network. 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. It is a join operation that is performed on the Reduce phase of a MapReduce job by aggregating data across multiple keys. It is a file format used to store large data sets in a distributed file system. It is a component of Hadoop used to schedule and manage MapReduce jobs across a cluster of nodes.

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.

Question 3

What is the key difference between batch processing and stream processing?

Stream processing is more scalable than batch processing Batch processing is faster than stream processing Batch processing is used for real-time data analysis while stream processing is used for handling large datasets Stream processing can handle larger datasets than batch processing Batch processing handles large volumes of data at once while stream processing handles data in real-time as it comes in

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.

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Big Data Scientist - Big Data Analytics Example Questions

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