最新のNCP-ADS問題集で試験を準備する

NVIDIA NCP-ADS 試験概要:

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 working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A) Reduce the dataset to a smaller sample size before processin
B) Use RAPIDS cuDF for GPU-accelerated data processing
C) Store the dataset in a relational database and query it using SQL
D) Scale up to a high-core-count CPU machine

2. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
B) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
C) Load the data into a Spark DataFrame and visualize using Apache Zeppelin
D) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly

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

4. A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A) Applying dropout regularization
B) Reducing the learning rate
C) Increasing the number of layers in the model
D) Removing data augmentation techniques

5. You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A) Use CPU-based implementations of the same frameworks for comparison.
B) Measure GPU utilization and memory usage, but ignore the network and disk I/O.
C) Use a batch size that is optimal for each framework's memory management.
D) Benchmark only the training phase of the deep learning model.
E) Use a common dataset for all frameworks to ensure comparability.

質問と回答：