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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Data preparation and processing	- Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services - Feature engineering 1. Feature selection and representation techniques 2. Transform and preprocess datasets
Topic 2: Designing ML solutions	- ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches - Framing ML problems 1. Translate business problems into ML tasks 2. Define success metrics and evaluation criteria
Topic 3: ML model development	- Model training and tuning 1. Train models using TensorFlow / Vertex AI 2. Hyperparameter tuning and optimization - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics
Topic 4: ML pipeline automation and orchestration	- Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines
Topic 5: Deployment and operations	- Model deployment 1. Deploy models using Vertex AI endpoints 2. Batch and online prediction systems - Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management

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Google Professional Machine Learning Engineer Sample Questions (Q359-Q364):

NEW QUESTION # 359
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

A. Decrease the maximum number of trials during subsequent training phases.
B. Set the early stopping parameter to TRUE
C. Decrease the number of parallel trials
D. Decrease the range of floating-point values
E. Change the search algorithm from Bayesian search to random search.

Answer: A,B

Explanation:
Reference:
https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning#early-stopping

NEW QUESTION # 360
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. 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
B. 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.
C. 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
D. 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.

Answer: D

Explanation:
* A fairness metric is a way to measure how well a machine learning model treats different groups of customers, such as by sex or age. A common fairness metric is accuracy, which is the proportion of correct predictions among all predictions. Accuracy across the sensitive features means calculating the accuracy for each group separately, and then comparing them. For example, if the model has 90% accuracy for male customers and 80% accuracy for female customers, there is a 10% accuracy gap that indicates potential bias against female customers.
* To investigate and mitigate potential bias, it is important to define a fairness metric and evaluate it on a test set. A test set is a subset of the data that is not used for training the model, but only for evaluating its performance. By joining the test set predictions with the sensitive features, you can calculate the fairness metric and see if it meets your requirements. For example, you may require that the accuracy gap between any two groups is less than 5%. If the fairness metric does not meet your requirements, you may need to adjust the model or the data to reduce bias.
* Option A is not the best answer because excluding the sensitive features and any meaningfully correlated features may not eliminate bias. For example, if salary is correlated with sex, and salary is also a predictive feature for the target variable, excluding both features may reduce the model accuracy and still leave some residual bias. Moreover, excluding features based on correlation may not capture the complex interactions and dependencies among the features that may affect bias.
* Option B is not the best answer because using the global attribution values for each feature of the model may not reflect the individual-level impact of the features on the predictions. Global attribution values are calculated by averaging the attribution values across all the data points, and they indicate how important each feature is for the overall model performance. However, they do not show how each feature affects each customer's prediction, which may vary depending on the values of the other features. For example, sex may have a low global attribution value, but it may have a high impact on some customers' predictions, especially if it interacts with other features such as salary or age.
* Option C is not the best answer because discarding the model and training the model again without a feature based on a single customer's attribution value may not be a robust or scalable way to mitigate bias. Attribution values are calculated by measuring how much each feature contributes to the prediction for a given data point, and they indicate how sensitive the prediction is to the feature value.
However, they do not show how the feature affects the overall fairness metric or the model accuracy.
For example, sex may have a high attribution value for a customer, but it may not affect the accuracy gap between the groups. Moreover, discarding and retraining the model based on a single customer's attribution value may not be feasible if there are many customers with high attribution values for different features.

NEW QUESTION # 361
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?

A. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
B. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
C. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
D. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Answer: B

Explanation:
Explanation/Reference: https://aws.amazon.com/step-functions/use-cases/

NEW QUESTION # 362
You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

A. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
B. Use latitude, longitude, and product type as features. Use profit as model output.
C. Use product type and the feature cross of latitude with longitude, followed by binning, as features.
Use revenue and expenses as model outputs.
D. Use product type and the feature cross of latitude with longitude, followed by binning, as features.
Use profit as model output.

Answer: D

Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

NEW QUESTION # 363
You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories. You have a labeled dataset in Cloud Storage. You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency. What should you do?

A. Train the model by using AutoML, and register the model in Vertex AI Model Registry. Configure your mobile application to send batch requests during prediction.
B. Train the model by using AutoML, and expose the model as a Vertex AI endpoint. Configure your mobile application to invoke the endpoint during prediction.
C. Train the model by using AutoML Edge, and export it as a Core ML model. Configure your mobile application to use the .mlmodel file directly.
D. Train the model by using AutoML Edge, and export the model as a TFLite model. Configure your mobile application to use the .tflite file directly.

Answer: C

NEW QUESTION # 364
......

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