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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 - Distributed training strategies - Model training and hyperparameter tuning
Topic 2: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification
Topic 3: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage
Topic 4: MLOps	19%	- Model deployment and serving - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance
Topic 5: 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 6: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Time-series analysis and anomaly detection - Data visualization and graph analytics

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q43-Q48):

NEW QUESTION # 43
A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.
B. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
C. Sort the data by timestamp and drop all sparse regions using df.dropna().
D. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.

Answer: B

NEW QUESTION # 44
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia TensorRT
B. Nvidia CUDA Profiler
C. Nvidia Riva
D. Nvidia Nsight Compute

Answer: D

NEW QUESTION # 45
A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A. Using a static dataset for monitoring instead of real-time production data
B. Implementing model performance tracking with drift detection metrics
C. Disabling logging to reduce system overhead and avoid storage costs
D. Logging only inference latency without tracking model predictions

Answer: B

NEW QUESTION # 46
You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?

A. Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.
B. Store the dataset in Apache Parquet format and load it using pandas to improve training performance.
C. Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
D. Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.

Answer: D

NEW QUESTION # 47
A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
B. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.
C. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
D. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.

Answer: D

NEW QUESTION # 48
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