NCP-ADS Reliable Test Tutorial, Latest NCP-ADS Exam Pattern

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q182-Q187):

NEW QUESTION # 182
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
B. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
C. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
D. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.

Answer: C

NEW QUESTION # 183
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Data Preparation
B. Data Understanding
C. Business Understanding
D. Model Building

Answer: C

NEW QUESTION # 184
You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A. Reduce the number of features used in training without profiling the actual bottlenecks.
B. Move all data from GPU memory to CPU memory before training the model.
C. Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
D. Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.

Answer: D

NEW QUESTION # 185
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
B. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
C. Convert both DataFrames to Pandas before performing the join for compatibility
D. Perform the join operation entirely in a CPU-based Spark environment for better stability

Answer: A

NEW QUESTION # 186
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
B. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
C. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
D. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.

Answer: D

NEW QUESTION # 187
......

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NCP-ADS Reliable Test Tutorial, Latest NCP-ADS Exam Pattern

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Latest NCP-ADS Exam Pattern - NCP-ADS Exam Labs

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q182-Q187):

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