NVIDIA-Certified-Professional Accelerated Data Science考題由資深的IT專家團隊研究出來的結果

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NVIDIA NCP-ADS 考試大綱主題:

最新的 NVIDIA-Certified Professional NCP-ADS 免費考試真題：

1. You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A) Convert the dataset to a NumPy array and manually replace missing values with the mean
B) Drop all rows containing missing values using Pandas before transferring data to the GPU
C) Use fillna() with a fixed value on the GPU using cuDF
D) Apply a deep learning-based imputation model before moving data to the GPU

2. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A) Increase the batch size of data loading without checking GPU memory usage
B) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
C) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
D) Use print() statements in the code to manually track execution times of different operation

3. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A) Scale all numerical features using min-max normalization
B) Apply log transformation to skewed features before standardizing them with z-score normalization
C) Use one-hot encoding to transform numerical features into categorical representations
D) Remove outliers before applying standardization

4. You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A) Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training
B) Test different model configurations on a CPU first before moving to the GPU for final evaluation
C) Train each possible model variation from scratch to evaluate accuracy and performance differences
D) Reduce training epochs significantly to save time, even if the model is underfitting

5. You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?

A) Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.
B) Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.
C) Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.
D) Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.

問題與答案：