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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A) df.astype('float16')
B) df['col'].apply(lambda x: np.float16(x))
C) df.convert_dtypes()
D) df.to_float16()

3. You are working on a machine learning project that requires selecting the optimal data types for each feature in your dataset to maximize performance and efficiency in an MLOps pipeline.
Which of the following data types is most suitable for GPU-accelerated machine learning workflows when working with large datasets on NVIDIA platforms?

A) Float32
B) Float64
C) Int32
D) String

4. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
B) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
C) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
D) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
E) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
F) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.

5. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A) Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
B) Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
C) Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
D) Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.

Solutions: