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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q209-Q214):

NEW QUESTION # 209
You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A. Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
B. Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
C. Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
D. Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.

Answer: D

NEW QUESTION # 210
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A. df['col'].apply(lambda x: np.float16(x))
B. df.convert_dtypes()
C. df.astype('float16')
D. df.to_float16()

Answer: C

NEW QUESTION # 211
You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?

A. Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.
B. Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.
C. Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.
D. Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.

Answer: D

NEW QUESTION # 212
You have a large-scale dataset consisting of IoT sensor readings collected at one-minute intervals across multiple locations. The dataset contains missing values and requires scaling before applying a machine learning model. You plan to use NVIDIA RAPIDS to preprocess and analyze the time-series data efficiently on GPUs.
Which of the following preprocessing steps is the most efficient approach using NVIDIA RAPIDS?

A. Use pandas for missing value imputation, then normalize the data using NumPy before converting to cuDF.
B. Use cuDF to handle missing values with GPU-accelerated interpolation and apply cuML's StandardScaler for feature scaling.
C. Use Dask for distributed missing value imputation and train a model using TensorFlow's CPU-based estimator.
D. Use pandas to fill missing values and scale the data, then convert it to cuDF for training.

Answer: B

NEW QUESTION # 213
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A. Using only virtual environments (venv) without managing GPU dependencies separately
B. Manually installing each package and its dependencies using pip
C. Using a single system-wide installation of CUDA and forcing all projects to use the same version
D. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies

Answer: D

NEW QUESTION # 214
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