NVIDIA NCP-ADS考試備考經驗，NCP-ADS在線題庫

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Data normalization and standardization 3. Synthetic data generation with RAPIDS
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. Docker and Conda environment management 3. CRISP-DM workflow execution
Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Memory optimization techniques (mixed precision, batching) 3. Multi-GPU training comparison 4. Feature engineering
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments

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最新的 NVIDIA-Certified Professional NCP-ADS 免費考試真題 (Q260-Q265):

問題 #260
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
B. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.

答案：C

問題 #261
A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A. Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
B. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
C. Use pandas.to_numeric() since pandas automatically handles type conversion.
D. Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.

答案：A

問題 #262
A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A. Increase the number of CPU cores and distribute training across multiple CPU threads.
B. Store all training data in RAM and load it directly to the CPU for processing.
C. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
D. Use a cloud instance with multiple GPUs and enable mixed-precision training.

答案：D

問題 #263
You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

A. The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
B. The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.
C. Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.
D. The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.

答案：B,C

問題 #264
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. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
B. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
C. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
D. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
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.

答案：B

問題 #265
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