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

Section	Weight	Objectives
Topic 1: Architect low-code AI solutions	12%	- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs - Identify use cases for low-code/no-code AI tools
Topic 2: Collaborate to manage data and models	16%	- Organize and prepare enterprise data 1. Work with structured, unstructured, and semi-structured data 2. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools - Manage datasets and features in Vertex AI - Address data privacy, compliance, and governance
Topic 3: Monitor and optimize AI solutions	16%	- Monitor model performance, fairness, and drift - Monitor data quality and pipeline health - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage
Topic 4: Automate and orchestrate ML pipelines	18%	- Design end-to-end ML workflows - Automate retraining and model updates - Use Vertex AI Pipelines, TFX, and other orchestration tools - Implement CI/CD for ML systems
Topic 5: Train and deploy models	20%	- Configure training jobs and environments - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns - Use Vertex AI deployment features and infrastructure
Topic 6: Scale prototypes into AI models	18%	- Select appropriate model architectures and frameworks - Optimize model performance and generalization - Design and run experiments - Work with foundation models and generative AI techniques

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Google Professional Machine Learning Engineer Sample Questions (Q205-Q210):

NEW QUESTION # 205
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 Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

Answer: D

NEW QUESTION # 206
You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?

A. 1. Export the data to Cloud Storage using the BigQuery command-line tool
2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
B. 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
2. Your application queries a Vertex AI endpoint where you deployed your model.
3. Responses are received by the caller application as soon as the model produces the prediction.
C. 1. Export your data to Cloud Storage using Dataflow.
2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
D. 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

Answer: C

NEW QUESTION # 207
You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID.
Query the lookup table to find the correct table ID for the data that you need.
B. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.
C. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.
D. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

Answer: D

Explanation:
https://cloud.google.com/data-catalog/docs/concepts/overview

NEW QUESTION # 208
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?

A. Create a Vertex Al Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.
B. Create a Vertex Al Workbench notebook to perform exploratory data analysis and create input features Save the features as a CSV file in Cloud Storage Import the CSV file as a new BigQuery table Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.
C. Create a Vertex Al Workbench notebook to perform exploratory data analysis Use IPython magics to create a new BigQuery table with input features, create the model and validate the results by using the create model, ml. evaluates, and ml. predict statements.
D. Run the create model statement from the BigQuery console to create an AutoML model Validate the results by using the ml. evaluate and ml. predict statements.

Answer: C

NEW QUESTION # 209
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 not a sufficient amount of training data.
B. There is training-serving skew in your production environment.
C. 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.
D. 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.

Answer: D

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.
References:
* 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 # 210
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