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AIF-C01 · Practice set 8 of 10

ML Techniques and Real-World Use Cases: 10 practice questions

10 questions · Untimed · Free

10 free AIF-C01 practice questions on ML Techniques and Real-World Use Cases, with an explanation for every answer. Untimed. The full mock exam and the timed version are in the app.

Set 8 · ML Techniques and Real-World Use Cases · 10 questions Read the lesson

  1. Question 1 of 10

    Which technique fits predicting a home's numeric sale price?

    1. ARegression, because the target is a numeric value
    2. BClassification, because the target is a predefined label
    3. CSpeech recognition, because the input must be audio
    4. DClustering, because the target is an unknown group
    Show the answer

    Regression fits a quantity on a numeric scale; classification instead returns a known class.

    Next → 1 / 10

  2. Question 2 of 10

    Which technique predicts whether an email belongs to the predefined spam class?

    1. AClassification for a predefined spam category
    2. BForecasting a future time-series quantity
    3. CRegression to an unrestricted numeric amount
    4. DClustering into groups with no predefined labels
    Show the answer

    Spam or not spam is a predefined categorical target, which gives the problem a classification shape.

    Next → 2 / 10

  3. Question 3 of 10

    A retailer wants to discover natural customer segments and has no segment labels. Which technique fits?

    1. ARegression toward a numeric customer target
    2. BClustering that discovers similar customer groups
    3. CSpeech recognition for customer recordings
    4. DClassification into predefined customer labels
    Show the answer

    Clustering discovers groups from similarity when predefined class labels are absent.

    Next → 3 / 10

  4. Question 4 of 10

    Which application analyzes camera images to find missing components on manufactured products?

    1. AForecasting for future product demand
    2. BNatural language processing for document sentiment
    3. CSpeech recognition for real-time transcription
    4. DComputer vision for visual anomaly detection
    Show the answer

    Computer vision consumes images and can detect visual defects or anomalies.

    Next → 4 / 10

  5. Question 5 of 10

    Which application type extracts entities and sentiment from written reviews?

    1. ANatural language processing for text analysis
    2. BComputer vision for object detection
    3. CRecommendation systems for item ranking
    4. DSpeech recognition for audio transcription
    Show the answer

    NLP analyzes human language and supports entity, phrase, topic, and sentiment extraction.

    Next → 5 / 10

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  7. Question 6 of 10

    What is the output direction of speech recognition?

    1. ASpeech maps spoken audio into written text
    2. BWritten text is converted into spoken audio
    3. CProduct activity is converted into recommendations
    4. DHistorical values are converted into future forecasts
    Show the answer

    Speech recognition transcribes audio; text-to-speech runs in the opposite direction.

    Next → 6 / 10

  8. Question 7 of 10

    Which outcome characterizes a knowledge-base application?

    1. AEstimating next quarter's demand from time-series data
    2. BRetrieving relevant information from organized content
    3. CGrouping customers without predefined segment labels
    4. DTranscribing a live conversation into written text
    Show the answer

    A knowledge base serves an information need by retrieving relevant content rather than predicting a future value.

    Next → 7 / 10

  9. Question 8 of 10

    A media application has viewing activity and an item catalog. It must personalize which videos each user sees next. Which application fits?

    1. AA recommendation system that ranks tailored items
    2. BA forecasting system that predicts total future demand
    3. CA speech system that transcribes the video's audio
    4. DA fraud system that flags suspicious payment events
    Show the answer

    Recommendations use user activity and item information to personalize products or content.

    Next → 8 / 10

  10. Question 9 of 10

    A finance team has two needs: flag suspicious card transactions now and estimate monthly transaction volume next year. Which mapping is correct?

    1. AFraud detection for flags; forecasting for future volume
    2. BSpeech recognition for flags; NLP for future volume
    3. CForecasting for flags; computer vision for future volume
    4. DKnowledge retrieval for flags; clustering for future volume
    Show the answer

    Fraud detection evaluates suspicious events, while forecasting estimates a future time-series outcome.

    Next → 9 / 10

  11. Question 10 of 10

    A development tool must manage intent, perform a sequence of repository tasks, and validate its work. Which application type fits?

    1. AAgentic AI for coordinating and validating multi-step actions
    2. BSpeech recognition transcribing a developer recording
    3. CForecasting estimating a future repository metric
    4. DClassification returning one predefined repository label
    Show the answer

    Agentic AI coordinates actions and progress toward a goal instead of returning one isolated prediction.

    Next → 10 / 10

  12. You’ve finished this set

    That’s 10 questions on ML Techniques and Real-World Use Cases. In the app the ones you miss come back exactly when you’re about to forget them.

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