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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

A) Use the same learning rate for all layers in a deep neural network
B) Use a fixed learning rate chosen through trial and error
C) Decrease the learning rate to zero at the end of training (learning rate scheduling)
D) Use learning rate warm-up followed by decay

2. A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A) Remove all rows with missing values and outliers to ensure only clean data is used.
B) Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
C) Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
D) Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.

3. In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A) NVIDIA Jetson
B) NVIDIA CUDA Toolkit
C) NVIDIA Deep Learning AI
D) NVIDIA RAPIDS

4. You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A) Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
B) Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
C) Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
D) Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.

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

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