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

Section	Objectives
Data preparation and processing	- Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques - Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services
Designing ML solutions	- Framing ML problems 1. Define success metrics and evaluation criteria 2. Translate business problems into ML tasks - ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches
ML model development	- Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI - Evaluation 1. Model validation strategies 2. Evaluate model performance metrics
Deployment and operations	- Model deployment 1. Batch and online prediction systems 2. Deploy models using Vertex AI endpoints - Monitoring and maintenance 1. Retraining and lifecycle management 2. Monitor model drift and performance
ML pipeline automation and orchestration	- Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines

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Google Professional Machine Learning Engineer Sample Questions (Q411-Q416):

NEW QUESTION # 411
You are training a custom language model for your company using a large dataset. You plan to use the ReductionServer strategy on Vertex Al. You need to configure the worker pools of the distributed training job.
What should you do?

A. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
B. Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators and choose a machine type that prioritizes bandwidth.
C. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs Configure the third worker pool to have GPUs: and use the reduction server container image.
D. Configure the machines of the first two pools to have TPUs. and to use a container image where your training code runs Configure the third pool to have TPUs: and use the reductionserver container image.

Answer: A

Explanation:
According to the web search results, Reduction Server is a faster GPU all-reduce algorithm developed at Google that uses a dedicated set of reducers to aggregate gradients from workers12. Reducers are lightweight CPU VM instances that are significantly cheaper than GPU VMs2. Therefore, the third worker pool should not have any accelerators, and should use a machine type that has high network bandwidth to optimize the communication between workers and reducers2. TPUs are not supported by Reduction Server, so the first two worker pools should have GPUs and use a container image that contains the training code12. The reduction-server container image is provided by Google and should be used for the third worker pool2.

NEW QUESTION # 412
You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

A. Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.
B. Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.
C. Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy it to a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.
D. Train an image clustering model using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.

Answer: A

Explanation:
Cloud Vision API offers pre-trained models specialized in identifying explicit or inappropriate content. By sending a copy of each image to a Cloud Storage bucket and triggering Cloud Vision through Cloud Run, the detection of explicit content is automated with minimal development time. Vertex AI custom models require more training data and infrastructure management, while AutoML-based solutions add more complexity.
Cloud Vision's existing capabilities meet the requirement effectively and are highly scalable for real-time moderation needs.

NEW QUESTION # 413
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

A. Store features in Bigtable as key/value data.
B. Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.
C. Store features in Vertex Al Feature Store.
D. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer: C

NEW QUESTION # 414
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

A. Categorical hinge
B. Sparse categorical cross-entropy
C. Categorical cross-entropy
D. Binary cross-entropy

Answer: D

NEW QUESTION # 415
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 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.
B. Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
C. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
D. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

Answer: A

NEW QUESTION # 416
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