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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q220-Q225):

NEW QUESTION # 220
You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A. Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
B. Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
C. Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.
D. Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.

Answer: D

NEW QUESTION # 221
You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A. df = (df - df.mean()) / df.std()
B. df = (df - df.min()) / (df.max() - df.min())
C. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
D. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)

Answer: C

NEW QUESTION # 222
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 nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
B. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
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 TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.

Answer: A

NEW QUESTION # 223
In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A. Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
B. Post-deployment data drift detection to assess model performance degradation.
C. Automated hyperparameter tuning during inference to optimize model performance.
D. Use of manual intervention for every model update to ensure accuracy.

Answer: A,B

NEW QUESTION # 224
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Z-score Standardization
B. Log Transformation
C. Min-Max Scaling
D. Robust Scaling

Answer: D

NEW QUESTION # 225
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