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Google Professional Machine Learning Engineer Certification Exam is a professional certification that tests the knowledge and skills of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is designed to evaluate the proficiency of candidates in various aspects of machine learning, including data processing, modeling, and deployment. Google Professional Machine Learning Engineer certification is offered by Google Cloud, a subsidiary of Google that provides cloud computing services to businesses and individuals.

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

Defining the input (features) and predicted output format
Assessing and communicating business impact
Defining business problems
Aligning with Google AI principles and practices (e.g. different biases)
Defining outcome of model predictions
Defining output use
Assessing data readiness
Define ML problem
Determination of when a model is deemed unsuccessful
Key results
Define business success criteria
Managing incorrect results
Identify risks to feasibility and implementation of ML solution. Considerations include:
Defining problem type (classification, regression, clustering, etc.)

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Google Professional Machine Learning Engineer exam is a certification program designed to test the skills and knowledge of individuals who work in the field of machine learning. Google Professional Machine Learning Engineer certification is intended for professionals who have a strong background in machine learning and who are interested in demonstrating their expertise in this field to potential employers.

Google Professional Machine Learning Engineer Sample Questions (Q93-Q98):

NEW QUESTION # 93
You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN), within the same module.
You are using the training_methodargument to select one of the two methods, and you are using the learning_rateand num_hidden_layersarguments in the DNN. You plan to use Vertex AI's hypertuning service with a budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance. What should you do?

A. Run one hypertuning job for 100 trials. Set num_hidden_layersand learning_rateas conditional hyperparameters based on their parent hyperparameter training_method.
B. Run one hypertuning job for 100 trials. Set num_hidden_layersas a conditional hyperparameter based on its parent hyperparameter training_method, and set learning_rateas a non-conditional hyperparameter.
C. Run two separate hypertuning jobs, a linear regression job for 50 trials, and a DNN job for 50 trials. Compare their final performance on a common validation set, and select the set of hyperparameters with the least training loss.
D. Run one hypertuning job with training_methodas the hyperparameter for 50 trials. Select the architecture with the lowest training loss, and further hypertune it and its corresponding hyperparameters tor 50 trials.

Answer: B

Explanation:
This approach allows you to explore both model architectures (linear regression and DNN) within a single hypertuning job while effectively managing hyperparameters. By setting num_hidden_layersas a conditional hyperparameter based on training_method, you ensure that the DNN-specific hyperparameters are only considered when the DNN architecture is selected. The learning_ratecan be tuned across both architectures as a non-conditional hyperparameter. This strategy maximizes the use of the 100 trials to identify the optimal architecture and hyperparameters while minimizing training loss and maximizing model performance.

NEW QUESTION # 94
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() < 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() < 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

A. There is training-serving skew in your production environment.
B. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
C. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
D. There is not a sufficient amount of training data.

Answer: C

Explanation:
The most likely problem is that the tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table. This is because the RAND() function generates a random number between 0 and 1 for each row, and the probability of a row being in both the training and validation tables is 0.2 * 0.8 0.16, which is not negligible. This means that some of the records that you use to validate your model are also used to train your model, which can lead to overfitting and poor generalization. Moreover, the probability of a row being in neither the training nor the validation table is 0.2 * 0.2 0.04, which means that you are wasting some of the data in your initial table and reducing the size of your datasets. A better way to split your data into training and validation sets is to use a hash function on a unique identifier column, such as the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE MOD(FARM_FINGERPRINT(id), 10) < 8); CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS (SELECT * FROM 'myproject.mydataset.mytable' WHERE MOD(FARM_FINGERPRINT(id), 10) > 8); This way, you can ensure that each row has a fixed 80% chance of being in the training table and a 20% chance of being in the validation table, without any overlap or omission.
Reference:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification BigQuery ML: Splitting data for training and testing BigQuery: FARM_FINGERPRINT function

NEW QUESTION # 95
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A. After building your model, use Shapley values to determine which features are the most informative.
B. Use an iterative dropout technique to identify which features do not degrade the model when removed.
C. Use Principal Component Analysis to eliminate the least informative features.
D. Use L1 regularization to reduce the coefficients of uninformative features to 0.

Answer: D

Explanation:
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview#sampled-shapley

NEW QUESTION # 96
You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

A. 1, Maintain the same machine type on the endpoint.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert add a compute node to the endpoint
B. 1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.
2 Set up a monitoring job and an alert for CPU usage
3 If you receive an alert investigate the cause
C. 1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.
2 Set up a monitoring job and an alert for GPU usage.
3 If you receive an alert investigate the cause.
D. 1 Change the machine type on the endpoint to have 32 vCPUs
2. Set up a monitoring job and an alert for CPU usage
3 If you receive an alert, scale the vCPUs further as needed

Answer: B

Explanation:
Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with 8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage. Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources. You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model. You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. Reference:
[Vertex AI Endpoint documentation]
[Autoscaling documentation]
[Monitoring documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

NEW QUESTION # 97
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
B. Reduce the learning rate and run the training process until the training loss stops decreasing.
C. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

Answer: B

NEW QUESTION # 98
......

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