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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) Reduce the number of features used in training without profiling the actual bottlenecks.
C) Move all data from GPU memory to CPU memory before training the model.
D) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.

2. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A) df["price"] = df["price"] / df["price"].max()
B) df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
C) df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
D) df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())

3. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
B) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
C) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
D) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.

4. You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A) Use manual inspection of model predictions periodically to assess accuracy in production.
B) Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.
C) Set up continuous model retraining pipelines triggered by data drift or performance degradation.
D) Implement model versioning and rollback procedures to maintain consistency in production models.

5. Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A) Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
B) Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
C) Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
D) Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.

Solutions: