The latest Preparing Text for AI Models certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Preparing Text for AI Models exam and earn Preparing Text for AI Models certification.
Exam Question 1
You’re training a model to summarize academic research papers in renewable energy. You check:
– Hugging Face → dataset of abstracts, 50k entries.
– Kaggle → dataset of energy-related patents, 2M entries.
=- Common Crawl → raw web pages with mixed content.
Which dataset is the better starting point, and why?
A. None of them
B. Common Crawl
C. Kaggle patents
D. Hugging Face abstracts
Correct Answer
D. Hugging Face abstracts
Exam Question 2
You’re fine-tuning a sentiment classifier for restaurant reviews in Spanish. You find a Kaggle dataset with 2M reviews, but notice:
– 40% are duplicates,
– 25% are in English,
– Labels are inconsistent (positive, neg, neutral, mixed).
What’s the best path forward?
A. Clean it, remove duplicates, filter for Spanish, normalize labels
B. Reject it entirely as too noisy
C. Use it as-is because size matters most
D. Translate the English entries to Spanish and add them in
Correct Answer
A. Clean it, remove duplicates, filter for Spanish, normalize labels
Exam Question 3
A startup wants to fine-tune an LLM on news articles from major publishers. They suggest scraping full articles daily.
What’s the best approach?
A. Scrape and publish the dataset on Hugging Face
B. Mix in scraped text with other public data
C. Scrape internally for non-commercial use
D. Use licensed datasets or APIs instead of scraping
Correct Answer
D. Use licensed datasets or APIs instead of scraping
Exam Question 4
You’re building a customer support bot for medical device troubleshooting. Hugging Face and Kaggle have no relevant datasets. The manufacturer’s public support site has hundreds of structured FAQs.
What’s the best move?
A. Fine-tune on general medical text instead
B. Scrape the FAQs, clean them, and build a dataset for fine-tuning
C. Use Common Crawl and filter for “medical device” keywords
D. Skip dataset building and stick to zero-shot prompting
Correct Answer
B. Scrape the FAQs, clean them, and build a dataset for fine-tuning
Exam Question 5
Your colleague gives you a dataset of product reviews in Parquet format. Your pipeline expects CSV. The Parquet file has nested JSON columns.
What’s the best approach?
A. Manually copy-paste rows into a new CSV file
B. Load with pandas, flatten JSON fields, then export as CSV
C. Write a shell script to rename the file extension from .parquet to .csv
D. Open it in Excel and resave as CSV
Correct Answer
B. Load with pandas, flatten JSON fields, then export as CSV
Exam Question 6
You’re asked to fine-tune an LLM to generate summaries of financial earnings reports. You find a dataset online with 12M documents labeled as “financial text.” On closer inspection, many are press releases, marketing material, or blog posts, and only about 15% are actual earnings reports. Some of the reports also contain Optical Character Recognition (OCR) errors.
In 2–4 sentences, explain how you would evaluate whether this dataset is worth using. Your answer should weigh quality, size, and relevance in relation to the summarization objective.
Correct Answer
Evaluate the dataset’s relevance by identifying and filtering for actual earnings reports, since only 15% match the summarization objective. Assess data quality by measuring OCR errors and cleaning or removing corrupted documents. Despite its 12 million documents, the dataset is worthwhile only if the relevant, high-quality subset is large enough to support effective fine-tuning.