LATEST PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM COST | PRACTICE PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAMS

Latest Professional-Machine-Learning-Engineer Exam Cost | Practice Professional-Machine-Learning-Engineer Exams

Latest Professional-Machine-Learning-Engineer Exam Cost | Practice Professional-Machine-Learning-Engineer Exams

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The Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Professionals who pass the exam demonstrate their ability to design and develop machine learning models that meet specific business requirements. Google Professional Machine Learning Engineer certification exam also covers various machine learning techniques such as deep learning, supervised and unsupervised learning, and reinforcement learning.

Google Professional Machine Learning Engineer Sample Questions (Q41-Q46):

NEW QUESTION # 41
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

  • A. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
    2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
  • B. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
    2 Dispatch an appropriately sized shuttle and indicate the required stops on the map
  • C. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
    2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
  • D. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
    2. Dispatch an available shuttle and provide the map with the required stops based on the prediction

Answer: C

Explanation:
This answer is correct because it uses a regression model to estimate the number of passengers at each shuttle station, which is a continuous variable. A tree-based regression model can handle both numerical and categorical features, such as the time of day, the location of the station, and the weather conditions. Based on the predicted number of passengers, the organization can dispatch a shuttle that has enough capacity and provide a map that shows the required stops. This way, the organization can optimize the shuttle service route and reduce the waiting time and fuel consumption. References:
* [Tree-based regression models]


NEW QUESTION # 42
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

  • A. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
  • B. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
  • C. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
  • D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

Answer: C


NEW QUESTION # 43
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

A)

B)

C)

D)

  • A. Option D
  • B. Option C
  • C. Option B
  • D. Option A

Answer: B


NEW QUESTION # 44
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

  • A. Create word embedding vectors that store edit distance with every other word.
  • B. Download word embeddings pre-trained on a large corpus.
  • C. Produce a set of synonyms for every word using Amazon Mechanical Turk.
  • D. Create one-hot word encoding vectors.

Answer: D

Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/


NEW QUESTION # 45
You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory dat a. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

  • A. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
  • B. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
  • C. Use the TFX ModelValidator tools to specify performance metrics for production readiness
  • D. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.

Answer: C


NEW QUESTION # 46
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