Valid NCP-ADS Test Materials & NCP-ADS Exam Format
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q183-Q188):
NEW QUESTION # 183
A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?
- A. Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
- B. Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
- C. Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32. - D. Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
- E. Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
- F. Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
- G. float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
Answer: D
NEW QUESTION # 184
You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?
- A. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
- B. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
- C. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
- D. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
Answer: C
NEW QUESTION # 185
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?
- A. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
- B. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
- C. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
- D. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
Answer: D
NEW QUESTION # 186
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?
- A. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
- B. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
- C. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
- D. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
Answer: B
NEW QUESTION # 187
A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?
- A. Use JSON format for easy readability and process it directly in cuDF.
- B. Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
- C. Store data in Parquet format and load it using cuDF in RAPIDS.
- D. Convert datasets into CSV format and store them in local disk storage.
Answer: C
NEW QUESTION # 188
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