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

Section	Objectives
Topic 1: Scaling prototypes into ML models	- Hyperparameter tuning - Training at scale (Distributed training, TPUs) - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
Topic 2: Serving and scaling models	- Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving - Batch prediction - Model optimization (Quantization, Distillation)
Topic 3: Collaborating within and across teams to manage data and models	- Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers
Topic 4: Monitoring ML solutions	- Model retraining strategies - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection
Topic 5: Automating and orchestrating ML pipelines	- CI/CD for ML systems - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines
Topic 6: Architecting low-code ML solutions	- AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models

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Google Professional Machine Learning Engineer Sample Questions (Q307-Q312):

NEW QUESTION # 307
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains
200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

A. Convert the records to GZIP CSV format.
B. Convert the records to JSON format.
C. Convert the records to Apache Parquet format.
D. Convert the records to XML format.

Answer: C

Explanation:
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.
Reference: https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

NEW QUESTION # 308
You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data user metadata and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

A. Read data to a Vertex Al Workbench notebook Use TensorFlow to train a two-tower model.
B. Read data to a Vertex AI Workbench notebook Use TensorFlow to train a matrix factorization model.
C. Load the data in BigQuery Use BigQuery ML to train a matrix factorization model.
D. Load the data in BigQuery Use BigQuery ML to tram an Autoencoder model.

Answer: C

Explanation:
BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data.
BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to train a matrix factorization model, which is a common technique for recommender systems. Matrix factorization models learn the latent factors that represent the preferences of users and the characteristics of items, and use them to predict the ratings or interactions between users and items. You can use the CREATE MODEL statement to create a matrix factorization model in BigQuery ML, and specify the matrix_factorization option as the model type. You can also use the ML.
RECOMMEND function to generate recommendations for new games based on the trained model. This solution requires the least amount of coding, as you only need to write SQL queries to train and use the model. References: The answer can be verified from official Google Cloud documentation and resources related to BigQuery and BigQuery ML.
* BigQuery ML | Google Cloud
* Using matrix factorization | BigQuery ML
* ML.RECOMMEND function | BigQuery ML

NEW QUESTION # 309
Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.
1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.
2 Predictions will be stored in BigQuery
3. The model will be stored in a Cloud Storage bucket and will be updated frequently You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

A. Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.
B. Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
C. Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
D. Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.

Answer: B

NEW QUESTION # 310
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

A. Amazon ECS
B. Amazon S3
C. Amazon ECR
D. AWS Secrets Manager
E. AWS CodeStar

Answer: B,C

NEW QUESTION # 311
You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
B. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
C. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
D. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

Answer: D

Explanation:
Labels are key-value pairs that you can attach to AI Platform resources such as jobs, models, and versions. Labels can help you organize your resources into descriptive categories that reflect your business needs. For example, you can use labels to indicate the owner, purpose, environment, or status of a resource. You can also use labels to filter the results when you list or monitor your resources on the Google Cloud Console or the Cloud SDK. Using labels can help you manage your resources in a clean and scalable way, without requiring separate projects or restrictive permissions.
Reference:
Using labels to organize AI Platform resources
Creating and managing labels

NEW QUESTION # 312
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