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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q90-Q95):

NEW QUESTION # 90
You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A. object - Allows flexibility in storing timestamps as strings for easier parsing.
B. bool - Provides a highly efficient way to store timestamps as binary values.
C. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
D. int32 - Uses less memory and can store high-precision timestamps efficiently.

Answer: C

NEW QUESTION # 91
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. Use seaborn with a sampled subset of the dataset to generate line plots.
B. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
C. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
D. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.

Answer: B

NEW QUESTION # 92
You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A. df_scaled = scaler.fit_transform(df)
B. scaler = cuml.preprocessing.StandardScaler()
C. df_scaled = (df - df.min()) / (df.max() - df.min())
D. df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
E. df_scaled = df / df.max()

Answer: B

NEW QUESTION # 93
Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)

A. TensorBoard
B. Python's cProfile
C. NVIDIA Nsight Systems
D. DLProf

Answer: C,D

NEW QUESTION # 94
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Business Understanding
B. Model Building
C. Data Understanding
D. Data Preparation

Answer: A

NEW QUESTION # 95
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