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

Section	Weight	Objectives
Train and deploy models	20%	- Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments
Monitor and optimize AI solutions	16%	- Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health - Monitor model performance, fairness, and drift
Scale prototypes into AI models	18%	- Optimize model performance and generalization - Design and run experiments - Work with foundation models and generative AI techniques - Select appropriate model architectures and frameworks
Automate and orchestrate ML pipelines	18%	- Automate retraining and model updates - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools - Design end-to-end ML workflows
Architect low-code AI solutions	12%	- Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs
Collaborate to manage data and models	16%	- Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data 1. Work with structured, unstructured, and semi-structured data 2. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools

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Google Professional Machine Learning Engineer Sample Questions (Q345-Q350):

NEW QUESTION # 345
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

A. Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
B. Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.
C. Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
D. Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.

Answer: B

NEW QUESTION # 346
You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?

A. Train an object detection model in Vertex Al custom training by using the annotated image data.
B. Train an object detection model in AutoML by using the annotated image data.
C. Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.
D. Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.

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". AutoML Vision2 is a service that allows you to train and deploy custom vision models for image classification and object detection. AutoML Vision simplifies the model development process by providing a graphical user interface and a no-code approach. You can use AutoML Vision to train an object detection model by using the annotated image data, and evaluate the model performance using metrics such as mean average precision (mAP) and intersection over union (IoU)3. Therefore, option B is the best way to quickly create an initial model for the given use case. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
AutoML Vision
Object detection evaluation
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 347
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A. Use an iterative dropout technique to identify which features do not degrade the model when removed.
B. Use Principal Component Analysis to eliminate the least informative features.
C. After building your model, use Shapley values to determine which features are the most informative.
D. Use L1 regularization to reduce the coefficients of uninformative features to 0.

Answer: D

Explanation:
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview#sampled-shapley

NEW QUESTION # 348
You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

A. 501*256+257*128+2 = 161154
B. 500*256+256*128+128*2 = 161024
C. 501*256+257*128+128*2=161408
D. 500*256*0 25+256*128*0 25+128*2 = 40448

Answer: B

Explanation:
The number of trainable weights in a DNN regression model with Keras APIs can be calculated by multiplying the number of input units by the number of output units for each layer, and adding the number of bias units for each layer. The bias units are usually equal to the number of output units, except for the last layer, which does not have bias units if the activation function is softmax1. In this code, the model has three layers: a dense layer with 256 units and relu activation, a dropout layer with 0.25 rate, and a dense layer with 2 units and softmax activation. The input shape is 500. Therefore, the number of trainable weights is:
* For the first layer: 500 input units * 256 output units + 256 bias units = 128256
* For the second layer: The dropout layer does not have any trainable weights, as it only randomly sets some of the input units to zero to prevent overfitting2.
* For the third layer: 256 input units * 2 output units + 0 bias units = 512 The total number of trainable weights is 128256 + 512 = 161024. Therefore, the correct answer is B.
References:
* How to calculate the number of parameters for a Convolutional Neural Network?
* Dropout (keras.io)

NEW QUESTION # 349
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
B. Deploy and version the model on Al Platform.
C. Use Dataflow with the SavedModel to read the data from BigQuery
D. Export the model to BigQuery ML.

Answer: D

NEW QUESTION # 350
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