NCA-GENL Best Training Material and Practice Test Q&A from CertLibrary.com

Question 1

You are working on developing an application to classify images of animals and need to train a neural model. However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Dropout
B. Early stopping
C. Random initialization
D. Transfer learning

Answer : D

Question 2

In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

A. K is responsible for computing the attention scores between the query and key vectors.
B. Q represents the query vector used to retrieve relevant information from the input sequence.
C. Q, K, and V are randomly initialized weight matrices used for positional encoding.
D. V is used to calculate the positional embeddings for each token in the input sequence.

Answer : B

Question 3

What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

A. To simplify the model's architecture, making it easier to interpret the results.
B. To artificially expand the dataset's size and improve the model's ability to generalize.
C. To reduce the computational resources required for training deep learning models.
D. To ensure perfect alignment and uniformity across all images in the dataset.

Answer : B

Question 4

In the context of data preprocessing for Large Language Models (LLMs), what does tokenization refer to?

A. Converting text into numerical representations.
B. Applying data augmentation techniques to generate more training data.
C. Splitting text into smaller units like words or subwords.
D. Removing stop words from the text.

Answer : C

Question 5

In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

A. Train the new model from scratch for each new category encountered.
B. Use rule-based systems to manually define the characteristics of each category.
C. Use a pre-trained language model with semantic embeddings.
D. Use a large, labeled dataset for each possible category.

Answer : C

Question 6

You are tasked with developing a text classification application but have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to enhance the performance of your new model?

A. Data augmentation
B. Gradient clipping
C. Batch normalization
D. Transfer learning

Answer : D

Question 7

Which library is used to accelerate data preparation operations on the GPU?

A. cuML
B. XGBoost
C. cuDF
D. cuGraph

Answer : C

Question 8

When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

A. To assess the computing resources required for fine-tuning
B. To uncover patterns and anomalies in the dataset
C. To select the appropriate learning rate for the model
D. To determine the optimum number of layers in the neural network

Answer : B

Question 9

Why might stemming or lemmatizing text be considered a beneficial preprocessing step in the context of computing TF-IDF vectors for a corpus?

A. It reduces the number of unique tokens by collapsing variant forms of a word into their root form, potentially decreasing noise in the data.
B. It enhances the aesthetic appeal of the text, making it easier for readers to understand the document’s content.
C. It increases the complexity of the dataset by introducing more unique tokens, enhancing the distinctiveness of each document.
D. It guarantees an increase in the accuracy of TF-IDF vectors by ensuring more precise word usage distinction.

Answer : A

Question 10

You are using RAPIDS and Python for a data analysis project. Which pair of statements best explains how RAPIDS accelerates data science?

A. RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU-GPU memory transfers.
B. RAPIDS focuses on CPU processing, providing faster speeds compared to traditional Python libraries.
C. RAPIDS is a Python library that provides functions to accelerate the PCIE bus throughput via word-doubling.
D. RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.

Answer : A

Question 11

When should one use data clustering and visualization techniques such as tSNE or UMAP?

A. When there is a need to handle missing values and impute them in the dataset.
B. When there is a need to perform regression analysis and predict continuous numerical values.
C. When there is a need to reduce the dimensionality of the data and visualize the clusters in a lower-dimensional space.
D. When there is a need to perform feature extraction and identify important variables in the dataset.

Answer : C

Question 12

Which of the following tasks is a primary application of XGBoost and cuML?

A. Inspecting, cleansing, and transforming data
B. Performing GPU-accelerated machine learning tasks
C. Training deep learning models
D. Data visualization and analysis

Answer : B

Question 13

In Exploratory Data Analysis (EDA) for Natural Language Understanding (NLU), which method is essential for understanding the contextual relationship between words in textual data?

A. Computing the frequency of individual words to identify the most common terms in a text.
B. Applying sentiment analysis to gauge the overall sentiment expressed in a text.
C. Generating word clouds to visually represent word frequency and highlight key terms.
D. Creating n-gram models to analyze patterns of word sequences like bigrams and trigrams.

Answer : D

Question 14

You are working with a data scientist on a project that involves analyzing and processing textual data to extract meaningful insights and patterns. There is not much time for experimentation and you need to choose a Python package for efficient text analysis and manipulation. Which Python package is best suited for the task?

A. NumPy
B. spaCy
C. Pandas
D. Matplotlib

Answer : B

Question 15

What are some methods to overcome limited throughput between CPU and GPU?

A. Using techniques like memory pooling.
B. Upgrade the GPU to a higher-end model.
C. Increase the clock speed of the CPU.
D. Increase the number of CPU cores.

Answer : A

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