Quiz NVIDIA - NCP-ADS - Efficient NVIDIA-Certified-Professional Accelerated Data Science Lab Questions

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization
Topic 2: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation
Topic 3: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Feature engineering 3. Multi-GPU training comparison 4. Memory optimization techniques (mixed precision, batching)
Topic 4: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Docker and Conda environment management 3. Benchmarking GPU workflows
Topic 5: MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
Topic 6: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q88-Q93):

NEW QUESTION # 88
You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A. Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.
B. Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
C. Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
D. Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.

Answer: A

NEW QUESTION # 89
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Ignore preprocessing and focus only on model training speed when comparing performance.
B. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
C. Run all benchmarks on a CPU to ensure fairness across frameworks.
D. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).

Answer: D

NEW QUESTION # 90
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Run each model on the same GPU type and instance size across both cloud platforms.
B. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
C. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
D. Limit the benchmarking tests to a single model architecture for each cloud platform.

Answer: A,B

NEW QUESTION # 91
Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A. NVIDIA Tesla
B. TensorRT
C. NVIDIA Jetson
D. NVIDIA A100

Answer: D

NEW QUESTION # 92
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
B. Store the entire dataset in a single large file to minimize the number of files in the directory.
C. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
D. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.

Answer: A

NEW QUESTION # 93
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