100% Pass Quiz NVIDIA - Trustable NCP-ADS Test Dates
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NCP-ADS New Dumps Questions | NCP-ADS Certification Exam Infor
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q139-Q144):
NEW QUESTION # 139
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)
- A. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
- B. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
- C. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
- D. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
- E. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
Answer: A,B
NEW QUESTION # 140
You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?
- A. Dask
- B. PySpark
- C. cuDF
- D. pandas
Answer: C
NEW QUESTION # 141
A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?
- A. A CPU-only instance with 128 GB of RAM for increased data processing speed.
- B. A cloud instance with NVIDIA A100 GPUs and NVLink support.
- C. A single-core CPU instance with high disk I/O throughput for faster data loading.
- D. A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.
Answer: B
NEW QUESTION # 142
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?
- A. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
- B. Use cuDF to manipulate data distributions and generate new data points based on existing features.
- C. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
Answer: B
NEW QUESTION # 143
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?
- A. Running training exclusively on CPU
- B. Disabling data parallelism
- C. Using mixed-precision training with Tensor Cores
- D. Prefetching data with the CPU while training on the GPU
Answer: C
NEW QUESTION # 144
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2026 NCP-ADS Test Dates : NVIDIA-Certified-Professional Accelerated Data Science Realistic NCP-ADS 100% Pass
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