NCP-ADS 무료 덤프문제 온라인 액세스

You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

• A.cuml.preprocessing.StandardScaler()
• B.cuml.preprocessing.OneHotEncoder()
• C.cuml.preprocessing.MinMaxScaler()
• D.cuml.preprocessing.LabelEncoder()

You are working on a deep learning project that requires a large dataset of high-resolution satellite images for training a convolutional neural network (CNN). You want to leverage NVIDIA technologies to efficiently acquire and manage the dataset.
Which of the following approaches is the most suitable?

• A.Use NVIDIA DALI (Data Loading Library) to stream and preprocess satellite image data efficiently for deep learning training.
• B.Use NVIDIA Modulus to generate synthetic satellite images instead of acquiring real-world data.
• C.Use NVIDIA RAPIDS cuDF to directly download and preprocess satellite images from an API in real time.
• D.Use NVIDIA DeepStream to acquire satellite images and store them in a structured dataset for machine learning.

You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

• A.Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
• B.Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
• C.Scale the cloud infrastructure vertically by increasing the size of the GPU in use.
• D.Use data parallelism techniques to split the workload evenly across multiple GPUs.
• E.Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.

You are working on a machine learning project that requires selecting the optimal data types for each feature in your dataset to maximize performance and efficiency in an MLOps pipeline.
Which of the following data types is most suitable for GPU-accelerated machine learning workflows when working with large datasets on NVIDIA platforms?

• A.Float32
• B.Float64
• C.Int32
• D.String

You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

• A.df = cudf.read_csv("data.parquet")
• B.df = cudf.to_gpu(pd.read_parquet("data.parquet"))
• C.df = cudf.read_parquet("data.parquet")
• D.df = pd.read_parquet("data.parquet")