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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q222-Q227):

NEW QUESTION # 222
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A. Enable pandas compatibility mode in cudf to improve performance.
B. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
C. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
D. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.

Answer: C

NEW QUESTION # 223
You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A. Pandas
B. Vaex
C. Dask
D. PySpark

Answer: A

NEW QUESTION # 224
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
B. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
C. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
D. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.

Answer: A

NEW QUESTION # 225
You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
B. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
C. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
D. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.

Answer: D

NEW QUESTION # 226
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 = (df - df.min()) / (df.max() - df.min())
B. df_scaled = scaler.fit_transform(df)
C. df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
D. df_scaled = df / df.max()
E. scaler = cuml.preprocessing.StandardScaler()

Answer: E

NEW QUESTION # 227
......

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