NCP-ADS最も有効な質問と解答で勉強

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NVIDIA NCP-ADS 試験シラバストピック:

NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題：

1. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
B) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
C) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
D) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.

2. You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A) Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
B) Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
C) Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
D) Manually compute the feature mean and variance on the CPU and apply the transformation before training.

3. You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A) int64 - Provides high precision and avoids potential overflow.
B) float32 - Reduces memory consumption compared to float64 while maintaining precision.
C) category - Optimizes storage and computation for categorical data in cuDF.
D) bool - Minimizes memory usage and supports efficient operations for categorical data.

4. You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A) NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
B) NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
C) NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
D) NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.

5. A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A) Use df.dropna() to remove all rows with missing values.
B) Replace missing values with zero using df.fillna(0).
C) Use df.fillna(df.mean()) to replace missing values with the column mean.
D) Convert missing values to a separate categorical class using df.fillna("missing").

質問と回答：