NVIDIA NCP-ADS Valid Study Notes | New NCP-ADS Braindumps Questions

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q36-Q41):

NEW QUESTION # 36
A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A. Encoding all numerical features as categorical variables to reduce dimensionality.
B. Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.
C. Using manual feature selection by dropping columns with low variance on a CPU.
D. Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.

Answer: B

NEW QUESTION # 37
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
B. Compare training times only without considering throughput, power efficiency, or memory utilization.
C. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
D. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.

Answer: A

NEW QUESTION # 38
You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?

A. Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.
B. Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.
C. Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
D. Store the dataset in Apache Parquet format and load it using pandas to improve training performance.

Answer: B

NEW QUESTION # 39
A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

A. Process metadata using PySpark on a CPU cluster before sending it to a GPU.
B. Use SQLite for metadata storage and process it with RAPIDS cuDF.
C. Store metadata in a traditional SQL database and query it using Pandas.
D. Use Dask-cuDF with Parquet files stored in an object storage system like S3.

Answer: D

NEW QUESTION # 40
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('int32')
B. df['item_id'] = df['item_id'].astype('string')
C. df['item_id'] = df['item_id'].astype('float64')
D. df['item_id'] = df['item_id'].astype('int64')

Answer: D

NEW QUESTION # 41
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NVIDIA NCP-ADS Valid Study Notes | New NCP-ADS Braindumps Questions

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New NCP-ADS Braindumps Questions | New NCP-ADS Test Pdf

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q36-Q41):

Free4Dump: Your Reliable NVIDIA NCP-ADS Exam Companion

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