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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Docker and Conda environment management 3. Benchmarking GPU workflows
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Detect anomalies in time series datasets 3. Use cuGraph for graph analytics
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Hyperparameter tuning 3. Multi-GPU training comparison 4. Feature engineering

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q165-Q170):

NEW QUESTION # 165
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT automatically builds training models from raw data without requiring pre-trained models.
B. TensorRT is mainly used for data visualization and not for model inference.
C. TensorRT is only compatible with image classification models and does not support other model types.
D. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.

Answer: D

NEW QUESTION # 166
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A. df['timestamp'] = df['timestamp'].astype('float32')
B. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
C. df['timestamp'] = df['timestamp'].astype('int64')
D. df['timestamp'] = df['timestamp'].astype('int32')

Answer: D

NEW QUESTION # 167
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.min()) / (df.max() - df.min())
B. df = (df - df.mean()) / df.std()
C. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
D. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)

Answer: D

NEW QUESTION # 168
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
B. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
D. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.

Answer: B

NEW QUESTION # 169
A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

A. Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
B. Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
C. Convert all numerical features to float64 to maximize precision for feature transformation.
D. Convert all categorical variables to string format for efficient processing in cuDF.

Answer: A

NEW QUESTION # 170
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