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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q109-Q114):

NEW QUESTION # 109
A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A. SQLite
B. Apache Spark with RAPIDS Accelerator
C. Pandas
D. Python multiprocessing module

Answer: B

NEW QUESTION # 110
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
B. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
C. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
D. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
E. Use Dask's default scheduler to distribute work across GPUs without any specific configuration

Answer: A,D

NEW QUESTION # 111
You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A. df = cudf.read_csv("data.parquet")
B. df = pd.read_parquet("data.parquet")
C. df = cudf.to_gpu(pd.read_parquet("data.parquet"))
D. df = cudf.read_parquet("data.parquet")

Answer: D

NEW QUESTION # 112
You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A. Float64
B. String
C. Int32
D. Int64

Answer: C

NEW QUESTION # 113
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
B. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.

Answer: C

NEW QUESTION # 114
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