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

1. You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A) Random Search
B) Bayesian Optimization
C) Grid Search
D) Gradient Descent

2. A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A) Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
B) Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
C) Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
D) Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.

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

4. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A) Vaex
B) PySpark
C) Pandas
D) Dask

5. A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A) Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
B) Use cudf.Series.random() to create independent random values for each column separately.
C) Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.
D) Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.

質問と回答：