NCP-ADS Prüfungs-Guide, NCP-ADS PDF Demo

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NCP-ADS Studienmaterialien: NVIDIA-Certified-Professional Accelerated Data Science & NCP-ADS Zertifizierungstraining

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NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen (Q228-Q233):

228. Frage
You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A. pandas
B. cuDF
C. Dask DataFrame with Dask-CUDA
D. Spark with RAPIDS Accelerator

Antwort: B

229. Frage
A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A. Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
B. Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
C. Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
D. Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.

Antwort: B

230. Frage
A retail company needs to design a real-time ETL workflow to process millions of sales records per second from multiple sources. The data needs to be cleaned, aggregated, and stored for real-time analytics.
Which NVIDIA-powered solution is best suited for this workload?

A. Use NumPy to perform array-based operations on sales data before inserting into a database
B. Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs
C. Use standard Apache Spark on CPU clusters and move transformed data to GPU storage
D. Perform ETL with cuGraph to model sales data as a graph before storage

Antwort: B

231. Frage
A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
B. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
C. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
D. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
E. Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
F. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.

Antwort: D

232. Frage
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
B. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
C. Run all benchmarks on a CPU to ensure fairness across frameworks.
D. Ignore preprocessing and focus only on model training speed when comparing performance.

Antwort: B

233. Frage
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