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

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

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Google Professional Machine Learning Engineer Sample Questions (Q54-Q59):

NEW QUESTION # 54
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

A. Use the BigQuery console to execute your query, and then save the query results into a new BigQuery table.
B. Write a Python script that uses the BigQuery API to execute queries against BigQuery.
Execute this script as the first step in your Kubeflow pipeline.
C. Locate the Kubeflow Pipelines repository on GitHub.
Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline.
Use the component to execute queries against BigQuery.
D. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries.

Answer: C

Explanation:
Kubeflow pipelines have different types of components, ranging from low- to high-level. They have a ComponentStore that allows you to access prebuilt functionality from GitHub.
https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb
https://v0-5.kubeflow.org/docs/pipelines/reusable-components/

NEW QUESTION # 55
You trained a model packaged it with a custom Docker container for serving, and deployed it to Vertex AI Model Registry. When you submit a batch prediction job, it fails with this error: "Error model server never became ready. Please validate that your model file or container configuration are valid. " There are no additional errors in the logs. What should you do?

A. Pull the Docker image locally, and use the docker run command to launch it locally. Use the docker logs command to explore the error logs
B. Change the healthRoute value in your model's configuration to /healthcheck
C. Add a logging configuration to your application to emit logs to Cloud Logging
D. Change the HTTP port in your model's configuration to the default value of 8080

Answer: A

NEW QUESTION # 56
Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

A. 1 Upload the audio files to Cloud Storage
2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions
3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
B. 1 Iterate over your local Tiles in Python
2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data
3. Call the speech: recognize API endpoint to generate transcriptions
4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
C. 1 Upload the audio files to Cloud Storage
2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.
3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method
D. 1 Iterate over your local files in Python
2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data
3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions

Answer: C

Explanation:
4 Call the Natural Language API by using the analyzesenriment method

NEW QUESTION # 57
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained. You will use Vertex AI Pipelines to run the pipeline. You need to decide which Google Cloud pipeline components to use. What components should you choose?

A. TabularDatasetCreateOp, CustomTrainingJobOp, and EndpointCreateOp
B. TextDatasetCreateOp, CustomTrainingJobOp, and ModelDeployOp
C. TextDatasetCreateOp, AutoMLTextTrainingOp, and EndpointCreateOp
D. TabularDatasetCreateOp. AutoMLTextTrainingOp, and ModelDeployOp

Answer: B

NEW QUESTION # 58
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

A. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
B. Create a new view with BigQuery that does not include a column with city information
C. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
D. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

Answer: A

NEW QUESTION # 59
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