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

1. A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A) Store data in Parquet format and load it using cuDF in RAPIDS.
B) Use JSON format for easy readability and process it directly in cuDF.
C) Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
D) Convert datasets into CSV format and store them in local disk storage.

2. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
B) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
C) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
D) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
E) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
F) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.

3. You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A) Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.
B) Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
C) Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.
D) Running each benchmark multiple times and averaging the results to account for variance in execution time.

4. A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A) Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
B) Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
C) Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
D) Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.

5. A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A) Run all benchmarks on a CPU to ensure fairness across frameworks.
B) Ignore preprocessing and focus only on model training speed when comparing performance.
C) Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
D) Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).

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