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

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

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Google Professional Machine Learning Engineer Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

A. Import your user events and then your product catalog to make sure you have the highest quality event stream
B. Use the "Other Products You May Like" recommendation type to increase the click-through rate
C. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Answer: C

Explanation:
Recommendations AI is a service that allows users to build, test, and deploy personalized product recommendations for their ecommerce websites. It uses Google's deep learning models to learn from user behavior and product data, and generate high-quality recommendations that can increase revenue, click-through rate, and customer satisfaction. One of the best practices for using Recommendations AI is to choose the right recommendation type for the business objective. The "Frequently Bought Together" recommendation type shows products that are often purchased together with the current product, and encourages users to add more items to their shopping cart. This can increase the average order value and the revenue for each transaction. The other options are not as effective or feasible for this objective. The "Other Products You May Like" recommendation type shows products that are similar to the current product, and may increase the click-through rate, but not necessarily the shopping cart size. Importing the user events and then the product catalog is not a recommended order, as it may cause data inconsistency and missing recommendations. The product catalog should be imported first, and then the user events. Using placeholder values for the product catalog is not a viable option, as it will not produce meaningful recommendations or reflect the real performance of the model. Reference:
Recommendations AI documentation
Choosing a recommendation type
Importing data to Recommendations AI

NEW QUESTION # 65
You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images. Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient, scalable, and low maintenance as possible. What should you do?

A. 1. Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label
2. Reference tfds.folder_dataset:ImageFolder in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.
B. 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
2. Reference tf.data.TFRecordDataset in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.
C. 1. Create a Jupyter notebook that uses an nt-standard-64 V100 GPU Vertex AI Workbench instance.
2. Write a Python script that creates sharded TFRecord files in a directory inside the instance.
3. Reference tf.data.TFRecordDataset in the training script.
4. Train the model by using the Workbench instance.
D. 1. Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex AI Workbench instance.
2. Write a Python script that copies the images into multiple Cloud Storage directories, where each. directory is named according to the corresponding label.
3. Reference tfds.foladr_dataset.ImageFolder in the training script.
4. Train the model by using the Workbench instance.

Answer: B

NEW QUESTION # 66
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A. 1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
B. 1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
C. 1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
D. 1 Build a notification system on Firebase

Answer: D

Explanation:
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold Explanation:
Firebase is designed for exactly this sort of scenario. Also, it would not be possible to create millions of pubsub topics due to GCP quotas https://cloud.google.com/pubsub/quotas#quotas
https://firebase.google.com/docs/cloud-messaging

NEW QUESTION # 67
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?

A. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
B. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
C. Configure Pub/Sub to stream the data into BigQuery.
D. Run a Dataflow streaming job to ingest the data into BigQuery.

Answer: C

Explanation:
https://cloud.google.com/blog/products/data-analytics/pub-sub-launches-direct-path-to-bigquery-for-streaming-analytics

NEW QUESTION # 68
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A. 1 = Cloud Natural Language API. 2 = Vertex Al, 3 = Cloud Vision API
B. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Natural Language
C. 1 = Vertex Al. 2 = Vertex Al. 3 = AutoML Vision
D. 1 = Vertex Al. 2 = Vertex Al. 3 = Cloud Natural Language API

Answer: D

Explanation:
Vertex AI is a unified platform for building and deploying ML models on Google Cloud. It supports both custom and AutoML models, and provides various tools and services for ML development, such as Vertex Pipelines, Vertex Vizier, Vertex Explainable AI, and Vertex Feature Store. Vertex AI can be used to create models for predicting ticket priority and resolution time, as these are domain-specific tasks that require custom training data and evaluation metrics. 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. Cloud Natural Language API can be used to perform sentiment analysis on the support tickets, as this is a general task that does not require domain-specific knowledge or jargon. The other options are not suitable for the given architecture. AutoML Natural Language and AutoML Vision are services that allow users to create custom natural language and vision models using their own data and labels. They are not needed for sentiment analysis, as Cloud Natural Language API already provides this functionality. Cloud Vision API is a pre-trained service that provides image analysis capabilities, such as object detection, face detection, text detection, and image labeling. It is not relevant for the support tickets, as they are not expected to have any images. References:
* Vertex AI documentation
* Cloud Natural Language API documentation

NEW QUESTION # 69
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