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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A. df['category_column'] = df['category_column'].astype('category')
B. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])
C. df['category_column'] = df['category_column'].one_hot_encode()
D. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)

Answer: A

NEW QUESTION # 27
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. float32 - Reduces memory consumption compared to float64 while maintaining precision.
B. int64 - Provides high precision and avoids potential overflow.
C. bool - Minimizes memory usage and supports efficient operations for categorical data.
D. category - Optimizes storage and computation for categorical data in cuDF.

Answer: D

NEW QUESTION # 28
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use datetime64[ns] for timestamp features to ensure high precision.
B. Use int32 for all numerical features to save memory.
C. Use int8 for categorical features where there are fewer than 256 categories.
D. Use object data types for categorical features to avoid type conversion overhead.
E. Use float64 for all numerical features to ensure maximum precision.

Answer: A,B,C

NEW QUESTION # 29
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
B. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
C. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
D. Use seaborn with a sampled subset of the dataset to generate line plots.

Answer: C

NEW QUESTION # 30
Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
B. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
C. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
D. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.

Answer: D

NEW QUESTION # 31
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