PROFESSIONAL-MACHINE-LEARNING-ENGINEER TEST DURATION | EXAM PROFESSIONAL-MACHINE-LEARNING-ENGINEER STUDY SOLUTIONS

Professional-Machine-Learning-Engineer Test Duration | Exam Professional-Machine-Learning-Engineer Study Solutions

Professional-Machine-Learning-Engineer Test Duration | Exam Professional-Machine-Learning-Engineer Study Solutions

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The web-based Google Professional-Machine-Learning-Engineer practice test software can be used through browsers like Firefox, Safari, and Google Chrome. The customers don't need to download or install any excessive plugins or software in order to use the web-based Google Professional-Machine-Learning-Engineer Practice Exam format. The web-based Professional-Machine-Learning-Engineer practice test software format is supported by different operating systems like Mac, iOS, Linux, Windows, and Android.

To be eligible for the Google Professional-Machine-Learning-Engineer Certification Exam, you need to have at least three years of experience in developing and deploying machine learning models on Google Cloud Platform or a similar platform. You should also have experience in programming languages such as Python, Java, or C++, and have a good understanding of machine learning concepts such as supervised and unsupervised learning, deep learning, and reinforcement learning.

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Google Professional Machine Learning Engineer Sample Questions (Q65-Q70):

NEW QUESTION # 65
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

  • A. Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.
  • B. Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
  • C. Import the TensorFlow model with BigQuery ML, and run the ml.predict function.
  • D. Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.

Answer: C


NEW QUESTION # 66
You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:
* Customer_id
* Product_id
* Date
* Days_since_last_purchase (measured in days)
* Average_purchase_frequency (measured in 1/days)
* Purchase (binary class, if customer purchased product on the Date)
You need to interpret your models results for each individual prediction. What should you do?

  • A. Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.
  • B. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.
  • C. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.
  • D. Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.

Answer: C


NEW QUESTION # 67
You work for a telecommunications company You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery, and the predictive features that are available for model training include
- Customer_id -Age
- Salary (measured in local currency) -Sex
-Average bill value (measured in local currency)
- Number of phone calls in the last month (integer) -Average duration of phone calls (measured in minutes) You need to investigate and mitigate potential bias against disadvantaged groups while preserving model accuracy What should you do?

  • A. Define a fairness metric that is represented by accuracy across the sensitive features Train a BigQuery ML boosted trees classification model with all features Use the trained model to make predictions on a test set Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.
  • B. Train a BigQuery ML boosted trees classification model with all features Use the ml. global explain method to calculate the global attribution values for each feature of the model If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature
  • C. Train a BigQuery ML boosted trees classification model with all features Use the ml. exflain_predict method to calculate the attribution values for each feature for each customer in a test set If for any individual customer the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.
  • D. Determine whether there is a meaningful correlation between the sensitive features and the other features Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features

Answer: D


NEW QUESTION # 68
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

  • A. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
  • B. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  • D. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

Answer: A

Explanation:
Explanation


NEW QUESTION # 69
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

  • A. RMSE
  • B. F-score where precision is weighed more than recall
  • C. F-score where recall is weighed more than precision
  • D. F1 score

Answer: C


NEW QUESTION # 70
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