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

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

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Google Professional Machine Learning Engineer Sample Questions (Q398-Q403):

NEW QUESTION # 398
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

A. Use AutoML Natural Language to extract custom entities for classification
B. Use the Cloud Natural Language API to extract custom entities for classification
C. Use the Al Platform Training built-in algorithms to create a custom model
D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm

Answer: A

Explanation:
AutoML Natural Language is a service that allows users to create custom natural language models using their own data and labels. It supports various natural language tasks, such as text classification, entity extraction, and sentiment analysis. AutoML Natural Language can be used to build a model to classify incoming calls by product, as it can extract custom entities from the transcribed calls and assign them to predefined categories. AutoML Natural Language also minimizes data preprocessing and development time, as it handles the data preparation, model training, and evaluation automatically. The other options are not as suitable for this scenario. AI Platform Training built-in algorithms are a set of pre-defined algorithms that can be used to train ML models on AI Platform, but they do not support natural language processing tasks. Cloud Natural Language API is a pre-trained service that provides natural language understanding capabilities, such as sentiment analysis, entity analysis, syntax analysis, and content classification. However, it does not support custom entities or categories, and may not recognize the product names from the calls. Building a custom model to identify the product keywords and then running them through a classification algorithm would require more data preprocessing and development time, as well as more coding and testing. Reference:
AutoML Natural Language documentation
AI Platform Training built-in algorithms documentation
Cloud Natural Language API documentation

NEW QUESTION # 399
You work for an online travel agency that also sells advertising placements on its website to other companies.
You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor.
You want to Implement the simplest solution. How should you configure the prediction pipeline?

A. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.
B. Embed the client on the website, and then deploy the model on AI Platform Prediction.
C. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.

Answer: C

NEW QUESTION # 400
You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

A. Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
B. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
C. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
D. Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

Answer: B

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". TensorFlow Transform2 is a library for preprocessing data with TensorFlow. TensorFlow Transform enables you to define and execute distributed pre-processing or feature engineering functions on large data sets, and then export the same functions as a TensorFlow graph for re-use during training or serving. TensorFlow Transform can handle both instance-level and full-pass data transformations. Apache Beam3 is an open source framework for building scalable and portable data pipelines. Apache Beam supports both batch and streaming data processing. Dataflow4 is a fully managed service for running Apache Beam pipelines on Google Cloud.
Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Therefore, option D is the best way to configure the preprocessing routine for the given use case, as it allows you to use the same preprocessing logic during model training and serving, and leverage the scalability and performance of Dataflow. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* TensorFlow Transform
* Apache Beam
* Dataflow
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 401
Your e-commerce platform serves customers in 14 languages. You need to build semantic product search so that a query in Spanish returns relevant products whose descriptions are written in English or Japanese. You want to avoid maintaining a separate index per language.
What should you do?

A. Fine-tune a monolingual English embedding model on translated product descriptions.
B. Generate embeddings for queries and product descriptions with a multilingual text embedding model, store them in a single Vector Search index, and retrieve by approximate nearest neighbor.
C. Translate every query into English with the Cloud Translation API, and use a keyword-based BM25 index over English descriptions.
D. Train a separate embedding model per language, and query all 14 indexes in parallel, merging results by score.

Answer: B

Explanation:
A multilingual embedding model maps semantically equivalent text from different languages into a shared vector space, so a Spanish query retrieves English and Japanese items from one index.
This avoids per-language indexes and the information loss and latency of a translation step in the query path.

NEW QUESTION # 402
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

A. Choose a different weight initialization type.
B. Change preprocessing to use n-grams.
C. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
D. Adjust hyperparameters related to the attention mechanism.

Answer: C

NEW QUESTION # 403
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