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The Google Professional-Machine-Learning-Engineer Exam consists of a variety of question types, including multiple choice, multiple select, and scenario-based questions. Professional-Machine-Learning-Engineer exam covers a range of topics, including data preparation, model training, model evaluation, model deployment, and monitoring and maintenance of machine learning models. Candidates are also expected to have a solid understanding of the Google Cloud Platform and its machine learning services, such as Cloud Machine Learning Engine and AutoML.
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Earning the Google Professional Machine Learning Engineer Certification demonstrates to employers and clients that you have the skills and knowledge needed to design and implement effective machine learning solutions on the Google Cloud Platform. It is a valuable credential for data scientists, software engineers, and other professionals who are interested in developing their skills in machine learning and cloud computing.
Google Professional Machine Learning Engineer exam is a certification offered by Google Cloud Platform that validates the skills of individuals in designing, building, and deploying machine learning models using Google Cloud technologies. Professional-Machine-Learning-Engineer Exam covers a range of topics including data preparation and analysis, machine learning algorithms and models, distributed computing, and deploying machine learning models.
NEW QUESTION # 116
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time.
Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
Answer: B
Explanation:
Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. It allows you to define your ML workflow as a sequence of steps, and then schedule it to run on Kubernetes. This makes it a good choice for scheduling the retraining of your model, as it can be easily scaled to handle large datasets and complex workflows.
NEW QUESTION # 117
You are building an ML model to detect anomalies in real-time sensor data. 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?
Answer: A
Explanation:
* Dataflow is a fully managed service for executing Apache Beam pipelines that can process streaming or batch data1.
* Al Platform is a unified platform that enables you to build and run machine learning applications across Google Cloud2.
* BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility3.
These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization. The other options are not as suitable because:
* DataProc is a service for running Apache Spark and Apache Hadoop clusters, which are not optimized for streaming data processing4.
* AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs5. However, it does not support custom models or real-time predictions.
* Cloud Bigtable is a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis.
* Cloud Functions is a serverless execution environment for building and connecting cloud services.
However, it is not suitable for storing or visualizing data.
* Cloud Storage is a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.
NEW QUESTION # 118
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis.
Which of the following services would both ingest and store this data in the correct format?
Answer: D
NEW QUESTION # 119
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
Answer: B
Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99
NEW QUESTION # 120
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?
Answer: B
Explanation:
BigQuery is a serverless, scalable, and cost-effective data warehouse that allows users to run SQL queries on large volumes of data. BigQuery Load is a tool that can ingest data from Cloud Storage into BigQuery tables. BigQuery SQL is a dialect of SQL that supports many of the same functions and operations as PySpark, such as window functions, aggregate functions, joins, and subqueries. By using BigQuery Load and BigQuery SQL, you can rebuild your ML pipeline for structured data on Google Cloud without having to manage any servers or clusters, and with faster performance and lower cost than using PySpark on Dataproc. You can also use BigQuery ML to create and evaluate ML models using SQL commands. Reference:
BigQuery documentation
BigQuery Load documentation
BigQuery SQL reference
BigQuery ML documentation
NEW QUESTION # 121
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