Quiz Google - Professional-Machine-Learning-Engineer - The Best Google Professional Machine Learning Engineer Reliable Test Bootcamp

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

Section	Objectives
Automating and orchestrating ML pipelines	- Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems - Triggering and scheduling pipelines
Monitoring ML solutions	- Logging and alerting (Cloud Monitoring) - Model retraining strategies - Performance monitoring and drift detection
Architecting low-code ML solutions	- AutoML capabilities and implementation - Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI)
Serving and scaling models	- Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Batch prediction - Online prediction (Vertex AI Prediction)
Collaborating within and across teams to manage data and models	- Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance
Scaling prototypes into ML models	- Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)

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Google Professional Machine Learning Engineer Sample Questions (Q152-Q157):

NEW QUESTION # 152
You are developing a natural language processing model that analyzes customer feedback to identify positive, negative, and neutral experiences. During the testing phase, you notice that the model demonstrates a significant bias against certain demographic groups, leading to skewed analysis results. You want to address this issue following Google's responsible AI practices. What should you do?

A. Use Vertex AI's model evaluation to assess bias in the model's predictions, and use post- processing to adjust outputs for identified demographic discrepancies.
B. Use Vertex Explainable AI to generate explanations and systematically adjust the predictions to address identified biases.
C. Implement a more complex model architecture that can capture nuanced patterns in language to reduce bias.
D. Audit the training dataset to identify underrepresented groups and augment the dataset with additional samples before retraining the model.

Answer: D

Explanation:
Auditing and augmenting the training dataset to improve representation of underrepresented groups directly addresses the root cause of model bias. This aligns with Google's responsible AI practices, which emphasize fairness by improving data quality and representation before relying on post-processing or model adjustments.

NEW QUESTION # 153
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables.
This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A. Manually combine all columns that contain a time signal into an array.
AIlow AutoML to interpret this array appropriately.
Choose an automatic data split across the training, validation, and testing sets.
B. Submit the data for training without performing any manual transformations.
AIlow AutoML to handle the appropriate transformations.
Choose an automatic data split across the training, validation, and testing sets.
C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column.
AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
D. Submit the data for training without performing any manual transformations.
Use the columns that have a time signal to manually split your data.
Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.

Answer: D

Explanation:
https://cloud.google.com/automl-tables/docs/data-best-practices#time
The time signal that is spread across multiple columns so manual split is required.
- If the time information is not contained in a single column, you can use a manual data split to use the most recent data as the test data, and the earliest data as the training data.

NEW QUESTION # 154
You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using:
headers = {"content-type": "application/json"}
json_response = requests.post('http:
//localhost:8501/v1/models/text_model:predict', data=data,
headers=headers)
What is the correct way to write the predict request?

A. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b'], [`c', `d'], [`e', `f']]})
B. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b', `c', `d', `e', `f']]})
C. data = json.dumps({"signature_name": "seving_default", "instances" [[`ab', `bc', `cd']]})
D. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b', `c'], [`d', `e', `f']]})

Answer: A

Explanation:
A negative number in the shape enables auto expand
(https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used- for-tensorflow).
Then the first number -1 out of the shape (-1, 2) speaks the number of 1 dimensional arrays within the tensor (and it can autoexpand) while the second numer (2) sets the number of elements in the inner array at 2.

NEW QUESTION # 155
You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

A. Create a batch prediction job by using the actual sales data, and configure the job settings to generate feature attributions. Compare the results in the report.
B. Generate counterfactual examples by using the actual sales data. Create a batch prediction job using the actual sales data and the counterfactual examples. Compare the results in the report.
C. Create a batch prediction job by using the actual sales data. Compare the predictions to the actuals in the report.
D. Train another model by using the same training dataset as the original, and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model. Compare the two sets of predictions in the report.

Answer: A

NEW QUESTION # 156
You work for a hospital. You received approval to collect the necessary patient data, and you trained a Vertex AI tabular AutoML model that calculates patients' risk score for hospital admission. You deployed the model. However, you're concerned that patient demographics might change over time and alter the feature interactions and impact prediction accuracy. You want to be alerted if feature interactions change, and you want to understand the importance of the features for the predictions. You want your alerting approach to minimize cost. What should you do?

A. Create a feature attribution drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
B. Create a feature drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
C. Create a feature attribution drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
D. Create a feature drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.

Answer: A

NEW QUESTION # 157
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