PMLE practice questions 21–30

1. Question 21 · 364 in bank

   You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

   • AUse a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
   • BUse a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
   • CUse a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
   • DUse a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKEGive the report to the logistics team each month so they can fine-tune inventory levels.

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   Answer: C

   Explanation

   This type of model is well-suited to predicting inventory levels because it can take into account trends and patterns in the data over time, such as seasonal fluctuations in demand or changes in customer behavior.

   Source: Adapted from community discussion — mil_spyro

   References

   • Google Cloud documentation

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2. Question 22 · 364 in bank

   You are building a TensorFlow model for a financial institution that predicts the impact of consumer spending on inflation globally. Due to the size and nature of the data, your model is long-running across all types of hardware, and you have built frequent checkpointing into the training process. Your organization has asked you to minimize cost. What hardware should you choose?

   • AA Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs
   • BA Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU
   • CA Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a non-preemptible v3-8 TPU
   • DA Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU

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   Answer: D

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3. Question 23 · 364 in bank

   You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

   • APosts can be compared to the keyword list much more quickly.
   • BNew problematic phrases can be identified in spam posts.
   • CA much longer keyword list can be used to flag spam posts.
   • DSpam posts can be flagged using far fewer keywords.

   Show answer

   Answer: B

   Explanation

   I vote B. Machine learning algorithms can learn to identify spam posts based on a wider range of factors, such as the content of the post, the user's behavior, and the context in which the post appears.

   Source: Adapted from community discussion — mil_spyro

   References

   • Google Cloud documentation

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4. Question 24 · 364 in bank

   You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon. The proposed architecture has the following flow:

   

   Which endpoints should the Enrichment Cloud Functions call?

   • A1 = AI Platform, 2 = AI Platform, 3 = AutoML Vision
   • B1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language
   • C1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API
   • D1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API

   Show answer

   Answer: C

   Explanation

   ANS: C https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow: A user writes a ticket to Firebase, which triggers a Cloud Function. -The Cloud Function calls 3 different endpoints to enrich the ticket: -An AI Platform endpoint, where the function can predict the priority. -An AI Platform endpoint, where the function can predict the resolution time. -The Natural Language API to do sentiment analysis and word salience. -For each reply, the Cloud Function updates the Firebase real-time database. -The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

   Source: Adapted from community discussion — Celia20210714

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 25 · 364 in bank

   One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?

   • AUse TensorFlow Data Validation to detect and flag schema anomalies.
   • BUse TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.
   • CUse tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
   • DUse custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

   Show answer

   Answer: A

   Explanation

   TensorFlow Data Validation (TFDV) is a library that can help you detect and flag anomalies in your dataset, such as changes in the schema or data types. https://www.tensorflow.org/tfx/data_validation/get_started

   Source: Adapted from community discussion — mil_spyro

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6. Question 26 · 364 in bank

   You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company’s catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event data. How should you build the recommendation system for the first version of the product?

   • ALaunch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.
   • BLaunch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.
   • CLaunch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.
   • DLaunch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

   Show answer

   Answer: B

   Explanation

   This is because you do not have any historical user event data, so you cannot use a collaborative filtering approach to build a recommender system. However, you can still use simple heuristics based on content metadata to recommend similar videos to users. For example, you could recommend videos that are in the same genre, have the same release date, or are from the same country. You should also start collecting user event data as soon as possible. This data will be valuable for training a recommender model in the future. Option D is a more complex approach that would require you to have more expertise in machine learning.(FOR THE FIRST VERSION OF THE PRODUCT)

   Source: Adapted from community discussion — tavva_prudhvi

   References

   • Google developer documentation

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7. Question 27 · 364 in bank

   You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?

   • AThe model is overfitting in areas with less traffic and underfitting in areas with more traffic.
   • BAUC is not the correct metric to evaluate this classification model.
   • CToo much data representing congested areas was used for model training.
   • DGradients become small and vanish while backpropagating from the output to input nodes.

   Show answer

   Answer: A

   Explanation

   Overfitting and Underfitting: Overfitting describes a model that performs well on the training data but struggles with unseen data. Underfitting signifies a model that hasn't learned enough patterns from the training data. Congested Traffic as Unseen Data: If the training data primarily consisted of scenarios with less traffic, the model might not have been exposed to enough examples of congested situations. This would make congested traffic act like unseen data, leading to underfitting and poor performance.

   Source: Adapted from community discussion — fitri001

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8. Question 28 · 364 in bank

   You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?

   • ADelete the rows that have missing values.
   • BApply feature crossing with another column that does not have missing values.
   • CPredict the missing values using linear regression.
   • DReplace the missing values with zeros.

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   Answer: C

   Explanation

   Preserves Information: Deleting rows (Option A) throws away valuable data, especially since every instance is important. Not Applicable Technique: Feature crossing (Option B) creates new features by multiplying existing features. It wouldn't address missing values directly. Zero Imputation Might Bias: Replacing missing values with zeros (Option D) can introduce bias if zeros have a specific meaning in the data (e.g., distance cannot be zero).

   Source: Adapted from community discussion — fitri001

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9. Question 29 · 364 in bank

   You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

   • ACreate the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
   • BCreate the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
   • CCreate the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
   • DCreate the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.

   Show answer

   Answer: B

   Explanation

   TFX for TensorFlow Models: TensorFlow Extended (TFX) is an end-to-end machine learning platform built on top of TensorFlow. It provides a set of pre-built components specifically designed for TensorFlow models, simplifying development and ensuring compatibility. Vertex AI Pipelines for Orchestration: Vertex AI Pipelines, a managed service from Google Cloud, is ideal for orchestrating ML pipelines. It integrates seamlessly with TFX and provides features like monitoring, scheduling, and scaling, reducing infrastructure maintenance needs.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation

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10. Question 30 · 364 in bank

    You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?

    • AUse the Vertex AI Training to submit training jobs using any framework.
    • BConfigure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.
    • CCreate a library of VM images on Compute Engine, and publish these images on a centralized repository.
    • DSet up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

    Show answer

    Answer: A

    Explanation

    Managed Service: It eliminates the need to administer a complex backend system, reducing your team's workload. Framework Agnostic: Vertex AI Training supports various frameworks like Keras, PyTorch, scikit-learn, and custom libraries, aligning with your data scientists' needs.

    Source: Adapted from community discussion — fitri001

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PMLE practice, question 21. 364 questions available.