For your convenience, ITCertMagic has prepared Google Professional Machine Learning Engineer exam study material based on a real exam syllabus to help candidates go through their exams. Candidates who are preparing for the Professional-Machine-Learning-Engineer Exam suffer greatly in their search for preparation material. You would not need anything else if you prepare for the exam with our Professional-Machine-Learning-Engineer Exam Questions.

Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

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The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.

Preparation Process

The candidates for the Google Professional Machine Learning Engineer certification can find everything they need to efficiently prepare for the qualifying test on the official website. The most recommended resource offered by the vendor is the Machine Learning Engineer learning path. It contains both lessons and practical labs for a comprehensive understanding of the exam content. Moreover, the students can take advantage of the sample questions designed to help the potential test takers familiarize themselves with the possible exam questions. Finally, the applicants can opt for the Machine Learning Engineer Prep Webinar to join the Google experts and recently certified professionals for the tips and insights on the Machine Learning models, data processing systems, solution quality, and more.

Google Professional Machine Learning Engineer Sample Questions (Q104-Q109):

NEW QUESTION # 104
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

• A. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
• B. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
• C. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
• D. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

Answer: D

Explanation:
https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/

NEW QUESTION # 105
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

• A. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
• B. This is a good result because the accuracy across both groups is greater than 80%.
• C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
• D. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

Answer: C

NEW QUESTION # 106
You have been asked to productionize a proof-of-concept ML model built using Keras. The model was trained in a Jupyter notebook on a data scientist's local machine. The notebook contains a cell that performs data validation and a cell that performs model analysis. You need to orchestrate the steps contained in the notebook and automate the execution of these steps for weekly retraining. You expect much more training data in the future. You want your solution to take advantage of managed services while minimizing cost. What should you do?

• A. Rewrite the steps in the Jupyter notebook as an Apache Spark job, and schedule the execution of the job on ephemeral Dataproc clusters using Cloud Scheduler.
• B. Extract the steps contained in the Jupyter notebook as Python scripts, wrap each script in an Apache Airflow BashOperator, and run the resulting directed acyclic graph (DAG) in Cloud Composer.
• C. Move the Jupyter notebook to a Notebooks instance on the largest N2 machine type, and schedule the execution of the steps in the Notebooks instance using Cloud Scheduler.
• D. Write the code as a TensorFlow Extended (TFX) pipeline orchestrated with Vertex AI Pipelines. Use standard TFX components for data validation and model analysis, and use Vertex AI Pipelines for model retraining.

Answer: D

NEW QUESTION # 107
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:
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
-The Cloud Function calls 3 different endpoints to enrich the ticket:
-An AI Platform endpoint, where the function can predict the priority.
-An AI Platform endpoint, where the function can predict the resolution time.
-The Natural Language API to do sentiment analysis and word salience.
-For each reply, the Cloud Function updates the Firebase real-time database.
-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

NEW QUESTION # 108
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

• A. Use a built-in model available on Al Platform Training
• B. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
• C. Build your custom container to run jobs on Al Platform Training
• D. Build your custom containers to run distributed training jobs on Al Platform Training

Answer: D

Explanation:
"ML framework and related dependencies are not supported by Al Platform Training" use custom containers "your model and your data are too large to fit in memory on a single machine " use distributed learning techniques

NEW QUESTION # 109
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