Free PDF Quiz NVIDIA - NCP-ADS Newest Exam Overviews

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q202-Q207):

NEW QUESTION # 202
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
B. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
C. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.
D. Install all required packages globally on the system using pip install without a virtual environment.

Answer: A

NEW QUESTION # 203
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use object data types for categorical features to avoid type conversion overhead.
B. Use float64 for all numerical features to ensure maximum precision.
C. Use int32 for all numerical features to save memory.
D. Use int8 for categorical features where there are fewer than 256 categories.
E. Use datetime64[ns] for timestamp features to ensure high precision.

Answer: C,D,E

NEW QUESTION # 204
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. Robust Scaling
B. Min-Max Scaling
C. Log Transformation
D. Z-score Standardization

Answer: A

NEW QUESTION # 205
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Load the dataset into Pandas and use Matplotlib for visualization
B. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration
C. Use TensorFlow and Keras to preprocess the data before performing EDA
D. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter

Answer: D

NEW QUESTION # 206
A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A. Apply log transformation to all numerical columns to force them into a uniform distribution.
B. Convert all numerical features to categorical values using binning.
C. Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
D. Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).

Answer: C

NEW QUESTION # 207
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