Benchmark datasets in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the purpose of benchmark datasets

Why do machine learning researchers use benchmark datasets?

A To train models on random data without labels

B To compare different models fairly using the same data

C To avoid testing models on real-world data

D To increase the size of training data by duplicating samples

💡 Hint

Think about why having a common dataset helps researchers.

Benchmark datasets provide a standard set of data so researchers can compare model performance fairly and consistently.

❓ Predict Output

intermediate

Output of loading a benchmark dataset

What is the output shape of the features when loading the Iris dataset using scikit-learn?

Prompt Engineering / GenAI

from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
print(X.shape)

A (150, 4)

B (3, 150)

C (150, 3)

D (4, 150)

💡 Hint

Check how many samples and features the Iris dataset has.

The Iris dataset has 150 samples and 4 features, so the shape of the data is (150, 4).

❓ Model Choice

advanced

Choosing a model for the MNIST dataset

You want to classify handwritten digits from the MNIST dataset. Which model is best suited for this task?

A Convolutional Neural Network (CNN)

B Linear Regression

C K-Means Clustering

D Decision Tree Regressor

💡 Hint

Consider the type of data and the task (image classification).

CNNs are designed to work well with image data and are commonly used for digit classification tasks like MNIST.

❓ Metrics

advanced

Evaluating model performance on benchmark datasets

Which metric is most appropriate to evaluate a classification model on the CIFAR-10 benchmark dataset?

A R-squared

B Mean Squared Error

C Accuracy

D Perplexity

💡 Hint

Think about the type of task CIFAR-10 represents.

CIFAR-10 is a classification dataset, so accuracy is a suitable metric to measure how many images are correctly classified.

🔧 Debug

expert

Identifying the error when loading a benchmark dataset

What error will this code raise when trying to load the Boston Housing dataset using scikit-learn?

Prompt Engineering / GenAI

from sklearn.datasets import load_boston
boston = load_boston()

A No error, dataset loads successfully

B ValueError: Dataset not found

C DeprecationWarning: load_boston is deprecated

D ImportError: cannot import name 'load_boston'

💡 Hint

Check recent changes in scikit-learn about this dataset.

The Boston Housing dataset was removed from scikit-learn due to ethical concerns, so trying to import load_boston raises ImportError.

Practice

1. What is the main purpose of benchmark datasets in machine learning?

easy

A. To speed up model training by using smaller data

B. To provide a standard way to test and compare models

C. To store user data for training

D. To create new machine learning algorithms

Solution

1. Step 1: Understand the role of benchmark datasets

   Benchmark datasets are used to test machine learning models on the same data so results can be compared fairly.

2. Step 2: Identify the correct purpose

   They are not for creating algorithms or storing user data, but for evaluation and comparison.

3. Final Answer:

   To provide a standard way to test and compare models -> Option B

4. Quick Check:

   Benchmark datasets = standard test data [OK]

Hint: Benchmark datasets test models fairly with known data [OK]

Common Mistakes:

• Thinking benchmark datasets create algorithms
• Confusing benchmark datasets with training data
• Assuming benchmark datasets speed up training

2. Which of the following is the correct way to load the popular MNIST benchmark dataset in Python using TensorFlow?

easy

A. from tensorflow.keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

B. import mnist
train_images, train_labels = mnist.load()

C. from sklearn.datasets import mnist
mnist.load()

D. load_mnist()

Solution

1. Step 1: Recall the TensorFlow MNIST loading syntax

   TensorFlow provides MNIST via keras.datasets with the load_data() method.

2. Step 2: Match the correct code snippet

   from tensorflow.keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() matches the correct import and loading syntax exactly.

3. Final Answer:

   from tensorflow.keras.datasets import mnist\n(train_images, train_labels), (test_images, test_labels) = mnist.load_data() -> Option A

4. Quick Check:

   TensorFlow MNIST load = keras.datasets.mnist.load_data() [OK]

Hint: TensorFlow MNIST loads with keras.datasets.mnist.load_data() [OK]

Common Mistakes:

• Using sklearn.datasets for MNIST (wrong library)
• Calling load() instead of load_data()
• Missing proper import statement

3. Given the following code snippet using the Iris dataset, what will be the output of print(data.target_names)?

from sklearn.datasets import load_iris
data = load_iris()
print(data.target_names)

medium

A. ['red', 'green', 'blue']

B. [0 1 2]

C. ['iris-setosa', 'iris-versicolor', 'iris-virginica']

D. ['setosa' 'versicolor' 'virginica']

Solution

1. Step 1: Understand the Iris dataset target names

   The Iris dataset target_names attribute contains the species names as numpy array strings without commas.

2. Step 2: Match the output format

   ['setosa' 'versicolor' 'virginica'] shows the correct array format with species names as strings without commas, matching sklearn output.

3. Final Answer:

   ['setosa' 'versicolor' 'virginica'] -> Option D

4. Quick Check:

   Iris target_names = species names array [OK]

Hint: Iris target_names shows species as array of strings [OK]

Common Mistakes:

• Confusing target_names with numeric labels
• Expecting commas inside numpy array print
• Using wrong species names

4. You try to load the CIFAR-10 dataset using this code but get an error:

from tensorflow.keras.datasets import cifar10
(train_images, train_labels), (test_images, test_labels) = cifar10.load()

What is the error and how to fix it?

medium

A. Error: SyntaxError due to missing parentheses, fix by adding () after load

B. Error: ImportError because cifar10 is not in keras.datasets, fix by installing extra package

C. Error: AttributeError because method is load_data(), fix by using cifar10.load_data()

D. No error, code runs fine

Solution

1. Step 1: Identify the method name for loading CIFAR-10

   The correct method to load CIFAR-10 in keras.datasets is load_data(), not load().

2. Step 2: Understand the error and fix

   Using cifar10.load() causes AttributeError. Changing to cifar10.load_data() fixes it.

3. Final Answer:

   Error: AttributeError because method is load_data(), fix by using cifar10.load_data() -> Option C

4. Quick Check:

   CIFAR-10 load method = load_data() [OK]

Hint: Use load_data() method to load datasets in keras.datasets [OK]

Common Mistakes:

• Using load() instead of load_data()
• Assuming cifar10 is not in keras.datasets
• Ignoring error message details

5. You want to compare two image classification models fairly. Which benchmark dataset should you choose and why?

hard

A. CIFAR-10 standard labeled image dataset for fair comparison

B. Unlabeled dataset for unsupervised learning

C. Small random dataset without standard labels

D. Single-class dataset to simplify training

Solution

1. Step 1: Understand the need for fair comparison

   Fair comparison requires a standard benchmark dataset with known labels and wide acceptance.

2. Step 2: Evaluate options for benchmark suitability

   CIFAR-10 is a popular benchmark with labeled images, suitable for comparing image classifiers fairly.

3. Final Answer:

   CIFAR-10 standard labeled image dataset for fair comparison -> Option A

4. Quick Check:

   Standard labeled dataset = fair model comparison [OK]

Hint: Choose standard labeled datasets for fair model comparison [OK]

Common Mistakes:

• Using unlabeled or small random datasets for comparison
• Choosing datasets with only one class
• Ignoring the need for standard benchmarks