100% Pass Quiz NVIDIA - NCP-ADS - High Pass-Rate NVIDIA-Certified-Professional Accelerated Data Science Authorized Certification

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Topic 2: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization
Topic 3: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Feature engineering 4. Memory optimization techniques (mixed precision, batching)
Topic 4: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Detect anomalies in time series datasets 3. Use cuGraph for graph analytics
Topic 5: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Topic 6: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Performance benchmarking and optimization 3. Memory and capacity evaluation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Min-Max Scaling
B. Robust Scaling
C. Log Transformation
D. Z-score Standardization

Answer: B

NEW QUESTION # 63
You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A. df['transaction_id'] = df['transaction_id'].astype('int8')
B. df['transaction_id'] = df['transaction_id'].astype('int64')
C. df['transaction_id'] = df['transaction_id'].astype('int32')
D. df['transaction_id'] = df['transaction_id'].astype('float32')

Answer: C

NEW QUESTION # 64
A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A. Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.
B. Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.
C. Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
D. Use cudf.Series.random() to create independent random values for each column separately.

Answer: A

NEW QUESTION # 65
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
B. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
C. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
D. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.

Answer: C

NEW QUESTION # 66
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Using only CPU-based data augmentation to keep the GPU dedicated to training
B. Increasing the batch size beyond the GPU's memory limit
C. Using mixed precision training with NVIDIA Tensor Cores
D. Disabling cuDNN optimizations to allow more flexibility in kernel execution

Answer: C

NEW QUESTION # 67
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