NCP-ADS Exam Materials, Reliable NCP-ADS Exam Preparation

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization - Data processing libraries selection and usage
Data Analysis	14%	- Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Data visualization and graph analytics
Machine Learning	15%	- Distributed training strategies - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation
Data Preparation	17%	- Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization
GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - GPU architecture and acceleration principles - Cloud GPU environments and deployment - Resource management and scaling strategies
MLOps	19%	- Pipeline automation and orchestration - Monitoring, logging and maintenance - Model deployment and serving - End-to-end workflow management

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q171-Q176):

NEW QUESTION # 171
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A. NVIDIA TensorRT Profiling
B. NVIDIA CUDA Profiler (nvprof)
C. NVIDIA Nsight Systems
D. NVIDIA nvidia-smi
E. NVIDIA DLA (Deep Learning Accelerator)

Answer: B,C

NEW QUESTION # 172
A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A. Apply log transformation to skewed features before standardizing them with z-score normalization
B. Remove outliers before applying standardization
C. Use one-hot encoding to transform numerical features into categorical representations
D. Scale all numerical features using min-max normalization

Answer: A

NEW QUESTION # 173
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.applymap() to create new synthetic features through complex mathematical functions.
B. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
C. Use cuDF to manipulate data distributions and generate new data points based on existing features.

Answer: C

NEW QUESTION # 174
A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

A. Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
B. Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.
C. Process data using Apache Spark on CPU before transferring results to GPU for model training.
D. Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.

Answer: D

NEW QUESTION # 175
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Optimize GPU performance by limiting the number of threads running on each GPU.
B. Utilize multi-GPU training to parallelize the workload, reducing training time.
C. Use a single cloud instance with the largest GPU available to ensure maximum performance.
D. Use larger batch sizes to make better use of GPU memory during model training.
E. Overclock the GPU to achieve higher computational speeds and improve training times.

Answer: B,D

NEW QUESTION # 176
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