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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
B. Downsample the dataset and analyze a subset using Pandas for efficiency.
C. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
D. Perform all analysis on the CPU to avoid potential GPU memory limitations.

Question 2

You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A. NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
B. NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
C. NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
D. NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.

Question 3

A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
B. Sort the data by timestamp and drop all sparse regions using df.dropna().
C. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
D. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.

Question 4

You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
B. df = (df - df.min()) / (df.max() - df.min())
C. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
D. df = (df - df.mean()) / df.std()

Question 5

A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A. Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
B. Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
C. Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.
D. Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.

Solutions: