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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q235-Q240):

NEW QUESTION # 235
You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A. df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
B. df["price"] = df["price"] / df["price"].max()
C. df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
D. df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )

Answer: C

NEW QUESTION # 236
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
B. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
C. Perform all analysis on the CPU to avoid potential GPU memory limitations.
D. Downsample the dataset and analyze a subset using Pandas for efficiency.

Answer: B

NEW QUESTION # 237
A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?

A. Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.
B. Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.
C. Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
D. Disable GPU memory caching to allow automatic CPU-based shuffle optimization.

Answer: B

NEW QUESTION # 238
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Rely on Matplotlib to generate static plots and update them every minute with a loop.
B. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
C. Precompute the time-series visualization with Dask and display it in a static HTML page.
D. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.

Answer: D

NEW QUESTION # 239
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. Convert all numerical features to float64 to maximize precision for feature transformation.
B. Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
C. Convert all categorical variables to string format for efficient processing in cuDF.
D. Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.

Answer: B

NEW QUESTION # 240
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