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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q67-Q72):

NEW QUESTION # 67
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
B. Use cuDF's .fillna() method to replace missing values in GPU memory
C. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
D. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution

Answer: B

NEW QUESTION # 68
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A. The batch size is too large, leading to excessive GPU memory utilization and slow processing.
B. The CUDA cores are overheating, leading to automatic throttling of computations.
C. The GPU is not powerful enough to process the deep learning model efficiently.
D. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.

Answer: D

NEW QUESTION # 69
You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
B. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
C. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold

Answer: D

NEW QUESTION # 70
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. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
D. Using a different precision setting for each framework to maximize performance per framework's capabilities.

Answer: C

NEW QUESTION # 71
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
B. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
C. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
D. Store data in SQLite databases and query it into pandas before converting it to cuDF.

Answer: B

NEW QUESTION # 72
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