NCP-ADS Exam Vce Free, NCP-ADS Study Center

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Feature engineering 3. Multi-GPU training comparison 4. Memory optimization techniques (mixed precision, batching)
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 3: 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 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. Benchmarking GPU workflows 2. CRISP-DM workflow execution 3. Docker and Conda environment management
Topic 6: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q143-Q148):

NEW QUESTION # 143
A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A. Use min-max scaling to transform all features into the range [0,1]
B. Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1
C. Keep the features as they are since machine learning models can handle varying scales naturally
D. Normalize the dataset by dividing each feature by its maximum absolute value

Answer: B

NEW QUESTION # 144
Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A. Hyperparameter tuning results.
B. Number of model parameters.
C. Training dataset bias.
D. GPU utilization and memory usage.

Answer: D

NEW QUESTION # 145
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
B. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
C. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
D. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.

Answer: A

NEW QUESTION # 146
A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A. Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
B. Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
C. Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
D. Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.

Answer: B

NEW QUESTION # 147
You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
B. df['category_column'] = df['category_column'].one_hot_encode()
C. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
D. df['category_column'] = df['category_column'].astype('category')

Answer: D

NEW QUESTION # 148
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