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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q19-Q24):

NEW QUESTION # 19
You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A. Fill outlier values with mean using pandas.Series.fillna().
B. Remove outliers completely using pandas.DataFrame.drop().
C. Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
D. Replace outliers with zero using pandas.Series.replace().

Answer: C

NEW QUESTION # 20
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A. The file format (CSV, JSON, or XML) of the dataset
B. Whether the dataset is stored on a cloud server or a local machine
C. The programming language used to preprocess the dataset
D. The number of missing values and inconsistencies in key columns

Answer: D

NEW QUESTION # 21
A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.
Which of the following cuGraph algorithms would be the best choice for this task?

A. Spectral Clustering
B. Betweenness Centrality
C. Breadth-First Search (BFS)
D. Weakly Connected Components

Answer: B

NEW QUESTION # 22
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Implement ETL processes using only SQL-based transformations within a relational database system.
B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
C. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
D. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

Answer: C

NEW QUESTION # 23
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"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
C. df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
D. df["price"] = df["price"] / df["price"].max()

Answer: B

NEW QUESTION # 24
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