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To obtain the Google Professional Machine Learning Engineer certification, candidates must pass a 2-hour exam that consists of multiple-choice and scenario-based questions. Professional-Machine-Learning-Engineer Exam evaluates the candidates on their ability to design and develop scalable, efficient, and secure machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification is recognized globally and is highly valued by employers as it demonstrates a professional's expertise and ability to work with cutting-edge technologies in the field of machine learning. Google Professional Machine Learning Engineer certification also provides access to Google's resources and community of machine learning professionals, making it a valuable asset for anyone looking to advance their career in this field.

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Google Professional Machine Learning Engineer Certification Exam is designed to test the skills and knowledge of individuals who are experts in the field of machine learning. Google Professional Machine Learning Engineer certification exam is a comprehensive test that covers a wide range of topics related to machine learning, such as data preparation, model building, model deployment, and monitoring. It is intended for individuals who have experience in developing and deploying machine learning models at scale.

Google Professional Machine Learning Engineer Sample Questions (Q70-Q75):

NEW QUESTION # 70
You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

A. 1 Use Vertex Al Experiments to train your model.
2 Register your model in Vertex Al Model Registry
3. Generate batch predictions in Vertex Al
B. 1 Upload your dataset to BigQuery
2. Use a Vertex Al custom training job to train your model
3 Generate predictions by using Vertex Al SDK custom prediction routines
C. 1 Use a Vertex Al Pipelines custom training job component to train your model
2. Generate predictions by using a Vertex Al Pipelines model batch predict component
D. 1 Create a Vertex Al managed dataset
2 Use a Vertex Ai training pipeline to train your model
3 Generate batch predictions in Vertex Al

Answer: C

NEW QUESTION # 71
You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data user metadata and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

A. Load the data in BigQuery Use BigQuery ML to tram an Autoencoder model.
B. Read data to a Vertex Al Workbench notebook Use TensorFlow to train a two-tower model.
C. Load the data in BigQuery Use BigQuery ML to train a matrix factorization model.
D. Read data to a Vertex AI Workbench notebook Use TensorFlow to train a matrix factorization model.

Answer: C

Explanation:
BigQuery is a serverless data warehouse that allows you to perform SQL queries on large-scale data.
BigQuery ML is a feature of BigQuery that enables you to create and execute machine learning models using standard SQL queries. You can use BigQuery ML to train a matrix factorization model, which is a common technique for recommender systems. Matrix factorization models learn the latent factors that represent the preferences of users and the characteristics of items, and use them to predict the ratings or interactions between users and items. You can use the CREATE MODEL statement to create a matrix factorization model in BigQuery ML, and specify the matrix_factorization option as the model type. You can also use the ML.RECOMMEND function to generate recommendations for new games based on the trained model.
This solution requires the least amount of coding, as you only need to write SQL queries to train and use the model. References: The answer can be verified from official Google Cloud documentation and resources related to BigQuery and BigQuery ML.
* BigQuery ML | Google Cloud
* Using matrix factorization | BigQuery ML
* ML.RECOMMEND function | BigQuery ML

NEW QUESTION # 72
You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

A. 1 Specify sampled Shapley as the explanation method with a path count of 5.
2 Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
B. 1 Specify Integrated Gradients as the explanation method with a path count of 50.
2. Deploy the model to Vertex Al Endpoints.
3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
C. 1 Specify Integrated Gradients as the explanation method with a path count of 5.
2 Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
D. 1. Specify sampled Shapley as the explanation method with a path count of 50.
2. Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

Answer: A

Explanation:
Sampled Shapley is a fast and scalable approximation of the Shapley value, which is a game-theoretic concept that measures the contribution of each feature to the model prediction. Sampled Shapley is suitable for online prediction requests, as it can return feature attributions with minimal latency. The path count parameter controls the number of samples used to estimate the Shapley value, and a lower value means faster computation. Integrated Gradients is another explanation method that computes the average gradient along the path from a baseline input to the actual input. Integrated Gradients is more accurate than Sampled Shapley, but also more computationally intensive. Therefore, it is not recommended for online prediction requests, especially with a high path count. Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model. Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal. Training- serving skew is the difference between the data used for training the model and the data used for serving the model. It can also affect the performance and accuracy of the model, and may indicate data quality issues or model staleness. Vertex AI Model Monitoring allows you to monitor training-serving skew on your deployed models and endpoints, and set up alerts and notifications when the skew exceeds a certain threshold.
However, this is not relevant to the question, as the question is about the feature attributions of the model, not the data distribution. References:
* Vertex AI: Explanation methods
* Vertex AI: Configuring explanations
* Vertex AI: Monitoring prediction drift
* Vertex AI: Monitoring training-serving skew

NEW QUESTION # 73
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

A. Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
B. Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
C. Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
D. Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

Answer: A

Explanation:
BigQuery ML is a service that allows you to create and train ML models using SQL queries. You can use BigQuery ML to train an AutoML regression model, which is a type of model that automatically selects the best features and architecture for your data. You can also specify Vertex AI as the model registry, which is a service that allows you to store and manage your ML models. By using Vertex AI as the model registry, you can easily deploy your model to a Vertex AI endpoint, which is a service that allows you to serve your ML models online and scale them automatically. By using BigQuery ML, Vertex AI model registry, and Vertex AI endpoint, you can deploy your model for online prediction as quickly as possible, without having to export, import, or retrain your model. References:
* BigQuery ML documentation
* Vertex AI documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 74
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+128*2=161408
B. 501*256+257*128+2 = 161154
C. 500*256*0 25+256*128*0 25+128*2 = 40448
D. 500*256+256*128+128*2 = 161024

Answer: D

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 # 75
......

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