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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q39-Q44):

NEW QUESTION # 39
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A. The GPU is not powerful enough to process the deep learning model efficiently.
B. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
C. The CUDA cores are overheating, leading to automatic throttling of computations.
D. The batch size is too large, leading to excessive GPU memory utilization and slow processing.

Answer: B

NEW QUESTION # 40
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

A. Disable batch inference to ensure each sample is processed independently for better accuracy
B. Use double-precision (FP64) computations to prevent numerical instability in inference
C. Increase the number of neurons in each layer to improve the model's accuracy
D. Use TensorRT for model optimization, including precision quantization and layer fusion

Answer: D

NEW QUESTION # 41
A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

A. Pandas, as it provides a simple API and works well for datasets that fit within system memory.
B. Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.
C. Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.
D. cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.

Answer: A

NEW QUESTION # 42
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
B. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
C. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
D. Store data in SQLite databases and query it into pandas before converting it to cuDF.

Answer: B

NEW QUESTION # 43
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
B. Use pandas to load the dataset and perform the operations using standard pandas functions
C. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
D. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods

Answer: D

NEW QUESTION # 44
......

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Exam NCP-ADS Fees - 100% Fantastic Questions Pool

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NCP-ADS Actual Test Pdf | Test NCP-ADS Passing Score

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q39-Q44):

To Prepare for the NVIDIA Exam, Get NVIDIA NCP-ADS Dumps

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