NVIDIA NCP-ADS valid study dumps : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A) Deploying a model without batching to ensure real-time responses
B) Disabling mixed-precision inference to avoid accuracy loss
C) Running inference on a CPU instead of a GPU to reduce power consumption
D) Using NVIDIA Triton Inference Server with model ensemble optimization

2. You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A) NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
B) NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
C) NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
D) NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.

3. You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A) Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
B) Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
C) Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
D) Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
E) Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.

4. A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A) Using Docker without GPU support and relying on CPU fallback when running TensorFlow
B) Compiling all dependencies into the host machine and using system-wide installations
C) Running the model in a local Python virtual environment and copying dependencies manually
D) Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies

5. 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) Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
B) Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
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

Solutions: