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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A) DLProf requires significant modifications to the source code to collect profiling data.
B) DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
C) DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
D) DLProf is primarily designed for debugging model accuracy rather than performance analysis.

2. You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A) Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
B) Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.
C) Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
D) Use data parallelism techniques to split the workload evenly across multiple GPUs.
E) Scale the cloud infrastructure vertically by increasing the size of the GPU in use.

3. Which of the following hardware components is most critical for accelerating the training of deep learning models?

A) Central Processing Unit (CPU)
B) Graphics Processing Unit (GPU)
C) Solid State Drive (SSD)
D) Random Access Memory (RAM)

4. Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A) Using Apache Spark for distributed data processing
B) Using SQLite as a local database for large-scale data analysis
C) Using Dask for parallel processing of large datasets
D) Using SQLAlchemy to interact with databases for large data processing
E) Using Excel to manipulate large datasets

5. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A) Rely only on training loss as the primary evaluation metric without considering validation performance.
B) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.
C) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
D) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.

Solutions: