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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A) Use int8 for categorical features where there are fewer than 256 categories.
B) Use int32 for all numerical features to save memory.
C) Use object data types for categorical features to avoid type conversion overhead.
D) Use float64 for all numerical features to ensure maximum precision.
E) Use datetime64[ns] for timestamp features to ensure high precision.

2. You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A) Use cuDF's .fillna() method to replace missing values in GPU memory
B) Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
C) Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
D) Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

3. You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A) Use manual inspection of model predictions periodically to assess accuracy in production.
B) Implement model versioning and rollback procedures to maintain consistency in production models.
C) Set up continuous model retraining pipelines triggered by data drift or performance degradation.
D) Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.

4. You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A) NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
B) NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
C) NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.
D) NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.

5. You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A) cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
B) cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
C) cuDF operations are executed lazily and require an explicit compute() call to execute transformations.
D) cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.

Solutions: