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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A) df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
B) df['category_column'] = df['category_column'].astype('category')
C) df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
D) df['category_column'] = df['category_column'].one_hot_encode()

2. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
C) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
D) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.

3. You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A) Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
B) Replace outliers with zero using pandas.Series.replace().
C) Remove outliers completely using pandas.DataFrame.drop().
D) Fill outlier values with mean using pandas.Series.fillna().

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) Pandas
C) PySpark
D) Dask

5. A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A) Reduce the model size by randomly pruning neurons without retraining.
B) Use mixed-precision inference with TensorRT to accelerate computation.
C) Enable CPU offloading to balance the workload between the CPU and GPU.
D) Increase the batch size significantly to improve GPU utilization.

Solutions: