AI-900 exam dumps: questions 101 to 200 of 285

Correct answers: A, C, E

• A. Correct.

  Deep learning techniques can automatically extract features from raw data, which is a major advantage over traditional machine learning techniques that often require manual feature engineering.

• B. Incorrect.

  Deep learning typically requires large amounts of data to perform well, especially for tasks such as image classification, making this statement incorrect.

• C. Correct.

  Deep learning leverages neural networks with multiple layers (deep neural networks) to learn complex patterns and relationships in the data.

• D. Incorrect.

  Deep learning is not specifically designed for structured tabular data; traditional machine learning algorithms often perform better for such data types.

• E. Correct.

  Deep learning can handle unstructured data such as images, audio, and text effectively, which is why it is commonly used in tasks like image classification or natural language processing.

Correct answers: A, C, E

• A. Correct.

  Correct. One of the key features of deep learning is its ability to automatically extract features from raw data, such as images, without the need for manual intervention.

• B. Incorrect.

  Incorrect. Deep learning typically requires a large amount of labeled data to achieve high performance, especially for tasks like image recognition.

• C. Correct.

  Correct. Deep learning excels at processing unstructured data types, including images, audio, and text, which makes it ideal for tasks like image classification.

• D. Incorrect.

  Incorrect. Deep learning uses deep architectures with multiple layers (hence the term 'deep') to learn complex patterns in data, not shallow architectures.

• E. Correct.

  Correct. Deep learning techniques are highly scalable and perform well with large datasets, improving accuracy as the amount of training data increases.

Correct answers: A, C, E

• A. Correct.

  Deep learning models can automatically extract features from raw data, such as patterns in images, without requiring manual feature engineering. This is one of its key strengths.

• B. Incorrect.

  Deep learning is not limited to structured data. It excels at working with unstructured data, such as images, audio, or text.

• C. Correct.

  Deep learning leverages multiple layers of neural networks to learn hierarchical representations of data, making it highly effective for modeling complex patterns.

• D. Incorrect.

  Deep learning often requires large datasets to perform well, as it needs significant data to train the numerous parameters in its models.

• E. Correct.

  Deep learning techniques are highly effective for tasks that involve unstructured data, such as image recognition, speech processing, and natural language understanding, making it suitable for this scenario.

Correct answers: A, C, E

• A. Correct.

  Deep learning techniques can automatically learn and extract features from raw data, such as edges, shapes, and patterns in images, making them highly effective for tasks like defect detection.

• B. Incorrect.

  Deep learning requires significant computational resources, such as GPUs or TPUs, especially for training with large datasets. This statement is incorrect.

• C. Correct.

  Deep learning excels at identifying complex patterns in large, labeled datasets, which is why it is well-suited for image classification tasks like defect detection.

• D. Incorrect.

  Deep learning models are often considered black-box models, meaning they lack interpretability and do not provide clear reasoning for their decisions. This is incorrect.

• E. Correct.

  Deep learning utilizes specialized architectures, such as convolutional neural networks (CNNs), which are highly effective for processing and analyzing image data.

Correct answers: A, C, E

• A. Correct.

  Deep learning techniques can automatically extract features from raw data without manual intervention, a crucial advantage when working with complex data like images.

• B. Incorrect.

  Deep learning typically requires large datasets to perform well, as more data enables the model to learn better representations.

• C. Correct.

  Deep learning models use multiple layers of artificial neural networks (often referred to as 'deep' networks), which allow them to learn hierarchical representations of data.

• D. Incorrect.

  Unlike traditional machine learning, deep learning reduces the need for extensive feature engineering, as it learns features directly from data.

• E. Correct.

  Deep learning models excel at processing unstructured data such as images, text, and audio, making them ideal for tasks like image classification.