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

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

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Google Professional Machine Learning Engineer Sample Questions (Q279-Q284):

NEW QUESTION # 279
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

A. * Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
B. * Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
C. * Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.
D. * Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

Answer: B

Explanation:
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing

NEW QUESTION # 280
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?

A. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
B. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.
C. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
D. Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

Answer: A

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". Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration and preprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Cloud Data Loss Prevention (DLP) API
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 281
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 Vertex Al Feature Store.
B. Store features in Bigtable as key/value data.
C. Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.
D. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Answer: A

Explanation:
Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to store features such as parcels delivered and truck locations over time, and retrieve them with low latency for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can also use Vertex AI Feature Store to retrieve historical data at a specific point in time for model training. Model training is a process of learning the parameters of a ML model from data. By using Vertex AI Feature Store, you can store the features with minimal effort, and avoid the complexity of managing your own data storage and serving system. Reference:
Vertex AI Feature Store documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 282
You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the dat a. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

A. Use BigQuery ML to run several regression models, and analyze their performance.
B. Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.
C. Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.
D. Read the data from BigQuery using Dataproc, and run several models using SparkML.

Answer: A

NEW QUESTION # 283
You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

A. Train the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.
B. Train the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile application to send batch requests during prediction.
C. Train the model by using AutoML Edge and export it as a Core ML model Configure your mobile application to use the mlmodel file directly.
D. Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly

Answer: C

Explanation:
* AutoML Edge is a service that allows you to train and deploy custom image classification models for mobile devices12. It supports exporting models as Core ML files, which are compatible with iOS applications3.
* Using a Core ML model directly on the device eliminates the need for network requests and reduces prediction latency. It also minimizes the cost of serving predictions, as there is no need to pay for cloud resources or network bandwidth.
* Option A is incorrect because sending batch requests during prediction does not reduce latency, as the requests still need to be processed by the cloud service. It also incurs more cost than using a local model on the device.
* Option C is incorrect because TFLite models are not compatible with iOS applications. TFLite models are designed for Android and other platforms that support TensorFlow Lite4.
* Option D is incorrect because exposing the model as a Vertex AI endpoint requires network requests and cloud resources, which increase latency and cost. It also does not leverage the benefits of AutoML Edge, which is optimized for mobile devices.

NEW QUESTION # 284
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