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Google Professional Machine Learning Engineer Certification Exam is a challenging and rewarding test that requires a deep understanding of machine learning principles and their practical application. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's ability to design, implement, and deploy machine learning models using Google Cloud Platform. It covers a broad range of topics, including data preprocessing, model training, hyperparameter tuning, and model evaluation.

To be eligible for the exam, candidates must have experience in developing and deploying machine learning models using Google Cloud Platform. They should also have experience with programming languages such as Python and SQL, and knowledge of machine learning concepts such as supervised and unsupervised learning, reinforcement learning, and deep learning.

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

Topic	Details
Topic 1	Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting for suitable hardware for training.
Topic 2	Architecting low-code AI solutions: This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.
Topic 3	Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.
Topic 4	Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.

Google Professional Machine Learning Engineer Sample Questions (Q140-Q145):

NEW QUESTION # 140
You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?

A. Vertex ML Metadata Vertex Al Experiments, and Vertex Al TensorBoard
B. Vertex ML Metadata Vertex Al Feature Store, and Vertex Al Vizier
C. Vertex Al Pipelines. Vertex Al Feature Store, and Vertex Al TensorBoard
D. Vertex Al Pipelines. Vertex Al Experiments, and Vertex Al Vizier

Answer: A

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "track the lineage of pipeline artifacts". Vertex ML Metadata2 is a service that allows you to store, query, and visualize metadata associated with your ML workflows, such as datasets, models, metrics, and executions. Vertex ML Metadata helps you track the provenance and lineage of your ML artifacts and understand the relationships between them. Vertex AI Experiments3 is a service that allows you to track and compare the results of your model training runs. Vertex AI Experiments automatically logs metadata such as hyperparameters, metrics, and artifacts for each training run. You can use Vertex AI Experiments to train your custom model using TensorFlow, PyTorch, XGBoost, or scikit-learn. Vertex AI TensorBoard4 is a service that allows you to visualize and monitor your ML experiments using TensorBoard, an open source tool for ML visualization.
Vertex AI TensorBoard helps you track the model's training parameters and the metrics per training epoch, and compare the performance of each version of the model. Therefore, option C is the best way to determine which Vertex AI services to include in the workflow for the given use case. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Vertex ML Metadata
* Vertex AI Experiments
* Vertex AI TensorBoard
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 141
You are developing an image recognition model using PyTorch based on ResNet50 architecture.
Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images. You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do?

A. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs. Prepare and submit a TFJob operator to this node pool.
B. Configure a Compute Engine VM with all the dependencies that launches the training. Train your model with Vertex AI using a custom tier that contains the required GPUs.
C. Create a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model.
D. Package your code with Setuptools, and use a pre-built container. Train your model with Vertex AI using a custom tier that contains the required GPUs.

Answer: D

NEW QUESTION # 142
You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer's stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

A. Automate the more complicated requests first because those require more of the agents' time.
B. Automate a blend of the shortest and longest intents to be representative of all intents.
C. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
D. Automate intents in places where common words such as payment only appear once to avoid confusing the software.

Answer: C

Explanation:
A is incorrect because you should not automate the higher value requests.
B is incorrect because live agents are better suited to handle these complicated requests.
C is correct because it enables a machine to handle the most simple requests and gives the live agents more opportunity to handle higher value requests.
D is incorrect because Dialogflow can handle the same word in multiple intents.
https://cloud.google.com/blog/products/ai-machine-learning/discover-card-how-we-designed-an-experiment-to-evaluate-conversational-experience-platforms

NEW QUESTION # 143
You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

A. Use product type and the feature cross of latitude with longitude, followed by binning, as features.
Use profit as model output.
B. Use latitude, longitude, and product type as features. Use profit as model output.
C. Use product type and the feature cross of latitude with longitude, followed by binning, as features.
Use revenue and expenses as model outputs.
D. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.

Answer: A

Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

NEW QUESTION # 144
You have trained a model by using data that was preprocessed in a batch Dataflow pipeline Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

A. Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Share this code with the end users of the endpoint.
B. Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Use the same code in the endpoint.
C. Batch the real-time requests by using a time window and then use the Dataflow pipeline to preprocess the batched requests. Send the preprocessed requests to the endpoint.
D. Perform data validation to ensure that the input data to the pipeline is the same format as the input data to the endpoint.

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". Dataflow2 is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataflow supports both batch and streaming data processing pipelines. However, if your use case requires real-time inference, you need to ensure that the data preprocessing logic is applied consistently between training and serving. One way to achieve this is to refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline, and use the same code in the endpoint. This way, you can avoid data skew and drift issues that might arise from using different preprocessing methods for training and serving. Therefore, option B is the best way to ensure the data preprocessing logic is applied consistently between training and serving. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Dataflow
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 145
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