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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A) Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
B) Use batch normalization to reduce memory usage by skipping some of the computations during training.
C) Decrease the size of the model by reducing the number of layers or the number of filters per layer.
D) Use automatic differentiation for every operation to save memory during the backpropagation phase.
E) Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.

2. You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A) df['timestamp'] = df['timestamp'].astype('float32')
B) df['timestamp'] = df['timestamp'].astype('int64')
C) df['timestamp'] = df['timestamp'].astype('int32')
D) df['timestamp'] = df['timestamp'].astype('datetime64[ms]')

3. You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A) Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
B) Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C) Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
D) Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

4. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
B) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
C) Reduce the number of features used in training without profiling the actual bottlenecks.
D) Move all data from GPU memory to CPU memory before training the model.

5. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A) Reduce the dataset size to a smaller sample to speed up processing.
B) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
C) Increase GPU clock speed manually to force higher processing power.
D) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.

Solutions: