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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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.read_csv("data.parquet")
C) df = pd.read_parquet("data.parquet")
D) df = cudf.to_gpu(pd.read_parquet("data.parquet"))

2. You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A) A dataset stored as an SQLite database file
B) A small dataset with fewer than 1,000 rows stored in an Excel file
C) A large, structured dataset stored in a Parquet format
D) A dataset consisting of unstructured text stored as plain .txt files

3. A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A) Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
B) Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
C) Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
D) Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.

4. A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A) Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.
B) Using manual feature selection by dropping columns with low variance on a CPU.
C) Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.
D) Encoding all numerical features as categorical variables to reduce dimensionality.

5. You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A) Moving Average Smoothing
B) One-Hot Encoding
C) Z-score (Standard Score) Analysis
D) Principal Component Analysis (PCA)

Solutions: