Quiz 2023 Google Professional-Machine-Learning-Engineer–Newest Exam Questions Answers

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The Google Professional Machine Learning Engineer Certification Exam is a professional certification that is designed to test an individual's proficiency in designing, building, and deploying machine learning models on the Google Cloud Platform. This certification is intended for individuals who have a thorough understanding of machine learning principles and experience with the Google Cloud Platform. The exam is designed to test an individual's ability to analyze and interpret data, design machine learning models, train and optimize models, and deploy models into production.

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Topics of Professional Machine Learning Engineer - Google

Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer exam dumps pdf will include the following topics:

ML Model Development
ML Pipeline Automation & Orchestration
ML Solution Monitoring, Optimization, and Maintenance

Google Professional Machine Learning Engineer Sample Questions (Q139-Q144):

NEW QUESTION # 139
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

A. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
B. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
C. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
D. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.

Answer: B

NEW QUESTION # 140
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

A. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
B. Vertex AI Pipelines and App Engine
C. Cloud Composer, BigQuery ML, and Vertex AI Prediction
D. Cloud Composer, Vertex AI Training with custom containers, and App Engine

Answer: B

NEW QUESTION # 141
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

A. Use a TPU with tf.distribute.TPUStrategy.
B. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
C. Create a custom training loop.
D. Increase the batch size.

Answer: C

Explanation:
This would allow you to tailor the training process to your specific needs and requirements, and it would also allow for more flexible experimentation with different training strategies.
Additionally, creating a custom training loop could result in faster training times compared to using a single GPU or the distributed training strategies currently available in Keras.

NEW QUESTION # 142
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

A. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
D. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage

Answer: C

NEW QUESTION # 143
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Run a BigQuery ML task to perform logistic regression for the classification
B. Configure AutoML Tables to perform the classification task
C. Use Al Platform Notebooks to run the classification model with pandas library
D. Use Al Platform to run the classification model job configured for hyperparameter tuning

Answer: B

Explanation:
https://cloud.google.com/automl-tables/docs/beginners-guide

NEW QUESTION # 144
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