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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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

NEW QUESTION # 143
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A. Increasing batch size without adjusting the optimizer settings
B. Using single-precision (FP32) calculations for better accuracy
C. Storing all training data in GPU memory at once
D. Using mixed precision training with automatic loss scaling

Answer: D

NEW QUESTION # 144
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A. Use pandas to load the dataset and perform the operations using standard pandas functions
B. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
C. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
D. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame

Answer: C

NEW QUESTION # 145
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
B. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
C. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
D. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.

Answer: C

NEW QUESTION # 146
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
B. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
C. Using a different precision setting for each framework to maximize performance per framework's capabilities.
D. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.

Answer: D

NEW QUESTION # 147
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using SQLite as a local database for large-scale data analysis
B. Using SQLAlchemy to interact with databases for large data processing
C. Using Excel to manipulate large datasets
D. Using Apache Spark for distributed data processing
E. Using Dask for parallel processing of large datasets

Answer: D,E

NEW QUESTION # 148
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