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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q30-Q35):

NEW QUESTION # 30
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A. Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
B. Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
C. Manually compute the feature mean and variance on the CPU and apply the transformation before training.
D. Use cupy.linalg.norm() to normalize each feature vector individually to unit length.

Answer: A

NEW QUESTION # 31
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
B. Convert both DataFrames to Pandas before performing the join for compatibility
C. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
D. Perform the join operation entirely in a CPU-based Spark environment for better stability

Answer: A

NEW QUESTION # 32
You are working with a large dataset containing millions of rows, and you need to store it efficiently for fast read and write operations while maintaining compatibility with CuDF and pandas.
Which of the following file formats is the best choice for efficient columnar storage and GPU-accelerated processing?

A. Parquet
B. TXT
C. JSON
D. CSV

Answer: A

NEW QUESTION # 33
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
B. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
C. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
D. Use pandas to load the dataset and perform the operations using standard pandas functions

Answer: A

NEW QUESTION # 34
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A. Force every operation to be recomputed from the raw dataset to ensure accurate results.
B. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
C. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
D. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.

Answer: C

NEW QUESTION # 35
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