NCP-ADS Reliable Exam Topics, NCP-ADS Valid Exam Dumps

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q95-Q100):

NEW QUESTION # 95
You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A. Use Pandas directly for data manipulation and visualization.
B. Perform all EDA using NumPy and SciPy for optimized array computations.
C. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
D. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.

Answer: C

NEW QUESTION # 96
You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A. One-Hot Encoding
B. Moving Average Smoothing
C. Principal Component Analysis (PCA)
D. Z-score (Standard Score) Analysis

Answer: D

NEW QUESTION # 97
You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A. Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
B. Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
C. Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
D. Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.

Answer: B

NEW QUESTION # 98
A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.
B. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
C. Sort the data by timestamp and drop all sparse regions using df.dropna().
D. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.

Answer: B

NEW QUESTION # 99
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Faster parallel processing for large datasets
B. Efficient handling of matrix operations in machine learning models
C. Lower cost of cloud infrastructure
D. Lower energy consumption compared to CPUs
E. Better for memory-intensive workloads

Answer: A,B

NEW QUESTION # 100
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