Google Professional-Machine-Learning-Engineer

Professional Machine Learning Engineer - Google Certified salary

The estimated average salary of Professional Machine Learning Engineer - Google is listed below:

• India: 8,580,000 INR
• Europe: 97,000 EURO
• United States: 114,000 USD
• England: 87,200 POUND

Who should take the Professional Machine Learning Engineer - Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.

The Google Professional-Machine-Learning-Engineer exam is for entry-level IT specialists and organization professionals with standard knowledge of the Google platform. The Google CCP certification validates the potential client's understanding of these topics and their skills; standard building principles, key services and also their use cases, security, and protection, as well as compliance with the Google model, paid versions, and prices. Google Professional-Machine-Learning-Engineer exam is the appropriate starting point for Google certification and is also an excellent resource for those interested in non-technical projects.

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

Exam Topics

The successful performance in the Google Professional Machine Learning Engineer certification test requires a good comprehension of its topics. The exam syllabus consists of six sections that are described below:

• Developing Machine Learning Models

  To answer the questions related to this section, the learners should know how to build, test, and train models. They should also possess the skills in scaling model training as well as serving, including distributed training and scaling prediction service (for instance, containerized serving, AI Platform Prediction, etc.).

• Automating & Orchestrating Machine Learning Pipelines

  This module encompasses one’s competency in designing & implementing training pipelines. This includes your ability to define the components, triggers, parameters, and compute needs; understanding of the orchestration framework; familiarity with the multi-Cloud or hybrid strategies; knowledge of system design involving the TFX components/Kubeflow DSL. The candidates should also possess the skills in implementing serving pipelines, including serving (online, caching, batch), testing for target performance, configuring trigger & pipeline schedules, among other skills. Apart from that, this part requires the students’ expertise in tracking & auditing metadata.

• Designing Data Preparation & Processing Systems

  The aim of this topic is to measure the individuals’ skills in exploring data (Exploratory Data Analysis). This involves their understanding of visualization, statistical fundamentals at scale, data quality & feasibility evaluation, as well as data constraint establishment. It also evaluates the ability of the test takers to build data pipelines, in particular, organize and optimize training datasets, validate data, handle missing data, handle outliers, etc. You should also know how to create the input features (feature engineering). This envisages the familiarity with encoding structured data types, feature selection, class imbalance, feature crosses, transformations, and more.

• Monitoring, Optimizing, and Maintaining Machine Learning Solutions

  This objective evaluates the competency of the applicants in monitoring and troubleshooting the Machine Learning solutions. The individuals should also be able to tune the performance of Machine Learning for training and serving in production. This involves the ability to optimize and simplify the input pipeline for training as well as knowledge of the simplification techniques.

• Architecting Machine Learning Solutions

  Here the examinees need to demonstrate their proficiency in designing reliable, scalable, and highly available Machine Learning solutions. Besides that, the test takers need to be capable of selecting the proper Google Cloud hardware components, including evaluating accelerator and compute options (for example, CPU, TPU, GPU, edge devices). Lastly, they need to have the expertise in designing an architecture that meets the security concerns across the industries/sectors.

• Framing Problems Related to Machine Learning

  Within this subject area, the candidates should be capable of translating business challenges into the Machine Learning use cases. They should also possess the skills in determining the Machine Learning problems, identifying the business success criteria, as well as defining risks to the feasibility of the Machine Learning solutions.

Today, Google Professional-Machine-Learning-Engineer certification exam enjoyed by many people and it can measure your ability. With the certificate of Google certified engineers, you will have a better job and a better future.

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Managing incorrect results
• Assessing and communicating business impact
• Defining outcome of model predictions
• Define ML problem
• Defining problem type (classification, regression, clustering, etc.)
• Defining output use
• Aligning with Google AI principles and practices (e.g. different biases)
• Identifying data sources
• Identifying nonML solutions
• Assessing data readiness
• Determination of when a model is deemed unsuccessful
• Defining business problems
• Identify risks to feasibility and implementation of ML solution. Considerations include:
• Assessing ML solution readiness
• Success metrics
• Key results
• Defining the input (features) and predicted output format
• Define business success criteria

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

Google Professional Machine Learning Engineer Sample Questions:

Question #1
You deployed a conversational application that uses a large language model (LLM). The application has 1,000 users. You collect user feedback about the verbosity and accuracy of the model 's responses. The user feedback indicates that the responses are factually correct but users want different levels of verbosity depending on the type of question. You want the model to return responses that are more consistent with users' expectations, and you want to use a scalable solution. What should you do?

A. Experiment with other proprietary and open-source LLMs. Perform A/B testing by setting each model as your application's default model. Choose a model based on the results.
B. Implement a keyword-based routing layer. If the user's input contains the words "detailed" or
"description," return a verbose response. If the user's input contains the word "fact." re-prompt the language model to summarize the response and return a concise response.
C. Ask users to indicate all scenarios where they expect concise responses versus verbose responses. Modify the application 's prompt to include these scenarios and their respective verbosity levels. Re-evaluate the verbosity of responses with updated prompts.
D. Ask users to provide examples of responses with the appropriate verbosity as a list of question and answer pairs. Use this dataset to perform supervised fine tuning of the foundational model.
Re-evaluate the verbosity of responses with the tuned model.

Question #2
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

A. Enable early stopping in your Vertex AI Training job.
B. Use the tf.distribute.Strategy API and run a distributed training job.
C. Increase the instance memory to 512 GB, and increase the batch size.
D. Replace the NVIDIA P100 GPU with a K80 GPU in the training job.

Question #3
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A. Create a Vertex AI Workbench user-managed notebook on a Dataproc Hub, and use the %%bigquery magic commands in Jupyter to query the tables.
B. Create a Vertex AI Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
C. Create a Vertex AI Workbench managed notebook on a Dataproc cluster, and use the spark- bigquery-connector to access the tables.
D. Create a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables.

Question #4
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?

A. Delete the rows that have missing values.
B. Predict the missing values using linear regression.
C. Apply feature crossing with another column that does not have missing values.
D. Replace the missing values with zeros.

Question #5
You have recently used TensorFlow to train a classification model on tabular data. You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

A. Import the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines create a pipeline that uses the BigquervQueryJobOp and the BigqueryPredictModelJobOp components.
B. Import the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint, and then uploads predictions to a BigQuery table.
C. Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataflowPvthonJobOp and the ModelBatchPredictOp components.
D. Import the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBacthPredictOp components.

Solutions: