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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA)
Topic 2: GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Resource management and scaling strategies - GPU architecture and acceleration principles - Cloud GPU environments and deployment
Topic 3: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools
Topic 4: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Data validation and quality assurance - Feature engineering and data type optimization
Topic 5: MLOps	19%	- Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management
Topic 6: Machine Learning	15%	- GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Distributed training strategies - Model evaluation and validation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q138-Q143):

NEW QUESTION # 138
You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A. Use a batch size that is optimal for each framework's memory management.
B. Use a common dataset for all frameworks to ensure comparability.
C. Measure GPU utilization and memory usage, but ignore the network and disk I/O.
D. Benchmark only the training phase of the deep learning model.
E. Use CPU-based implementations of the same frameworks for comparison.

Answer: A,B

NEW QUESTION # 139
A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A. Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
B. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
C. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
D. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.

Answer: C

NEW QUESTION # 140
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
B. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
C. Force every operation to be recomputed from the raw dataset to ensure accurate results.
D. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.

Answer: A

NEW QUESTION # 141
You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A. category - Optimizes storage and computation for categorical data in cuDF.
B. float32 - Reduces memory consumption compared to float64 while maintaining precision.
C. bool - Minimizes memory usage and supports efficient operations for categorical data.
D. int64 - Provides high precision and avoids potential overflow.

Answer: A

NEW QUESTION # 142
You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

A. NVIDIA DALI does not support data augmentation and is only used for raw data loading.
B. NVIDIA DALI can offload image preprocessing tasks to the GPU, reducing CPU bottlenecks and improving training throughput.
C. NVIDIA DALI only supports preprocessing images and does not include any functionality for video or text data.
D. NVIDIA DALI is an alternative to cuDF for GPU-accelerated tabular data processing.

Answer: B

NEW QUESTION # 143
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