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NVIDIA NCP-ADS Exam Overview:

NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A) Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
B) Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
C) Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
D) Use NumPy for data transformations before converting the dataset into cuDF for final storage.

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 all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
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 categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.

3. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
B) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
C) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
D) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

4. You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A) Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
B) Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
C) Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
D) Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing

5. You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A) Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.
B) Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.
C) Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.
D) Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.

Solutions: