Question 1

A generative AI model is deployed in a real-time application but is experiencing latency issues, causing delays in response time. Which of the following actions would most effectively address this performance problem?

Options :

A : Upgrade the model to a more complex version

B : Increase the batch size used during inference

C : Optimize the model-s inference process by reducing the number of layers

D : Adjust the learning rate used during model training

Answer: C

Question 2

Which of the following best describes Word2vec?

Options :

A : A programming language used to build artificial intelligence models

B : A statistical technique used to analyze word frequency in a text corpus.

C : A deep learning algorithm used to generate word embeddings from text data.

D : A database management system designed for storing and querying word data.

Answer: C

Question 3

You are developing a machine learning model for predictive maintenance in a manufacturing plant. The model needs to analyze sensor data from thousands of machines in real-time, identifying patterns that indicate potential failures. This requires processing a continuous stream of data, some of which exceeds the available memory capacity. Which approach is most suitable for handling this complex scenario?

Options :

A : Implementing a distributed processing framework like Apache Spark with memory-efficient data structures

B : Processing only a random subset of the data to reduce memory usage

C : Using a batch processing system to process the data at fixed intervals

D : Storing all incoming data in memory and processing it directly

Answer: A

Question 4

You are tasked with improving an AI model designed to assist medical professionals by providing diagnostic suggestions based on patient data. However, the model has been found to give inaccurate suggestions in certain rare cases, which could lead to incorrect treatments. The senior team member suggests conducting an experiment to evaluate the model's performance on rare cases and determine if incorporating human feedback can enhance its accuracy. Which experimental approach would be most effective in improving the model’s accuracy on rare cases?

Options :

A : Increase the overall size of the training dataset by including more common cases.

B : Conduct targeted fine-tuning of the model using a dataset enriched with rare cases and assess performance.

C : Train the model with reinforcement learning using feedback from common cases.

D : Use unsupervised learning techniques to identify patterns in rare cases without human intervention.

Answer: B

Question 5

You are tasked with deploying a generative AI model in a real-time application with strict latency requirements. The model is large and complex, making it difficult to meet the latency targets. Which approach would be most effective in ensuring the model meets the latency requirements without significantly compromising accuracy?

Options :

A : Use NVIDIA TensorRT to optimize the model for inference.

B : Reduce the size of the model by pruning layers.

C : Implement quantization-aware training to reduce the model-s precision.

D : Switch to a simpler, less complex model architecture.

Answer: A

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