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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Data Preparation | 17% | - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Data validation and quality assurance |
| Topic 3: Data Analysis | 14% | - Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection |
| Topic 4: Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - Dependency management and containerization - GPU-accelerated ETL workflows - Data processing libraries selection and usage |
| Topic 5: MLOps | 19% | - End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance |
| Topic 6: Machine Learning | 15% | - Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies - GPU-accelerated ML frameworks and algorithms |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?
A) Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
B) Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
C) Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
D) Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
2. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?
A) Apache Spark with its default CPU-based execution
B) RAPIDS + Dask for distributed GPU-accelerated ETL
C) Pandas with multiprocessing
D) Hadoop MapReduce
3. A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?
A) Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.
B) Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.
C) Use CPU-based data augmentation techniques before transferring the data to the GPU for training.
D) Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.
4. You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?
A) Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B) Use cuDF to manipulate data distributions and generate new data points based on existing features.
C) Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
5. 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) Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
B) Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
C) Enable pandas compatibility mode in cudf to improve performance.
D) Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: B |
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