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Google Professional Machine Learning Engineer Certification Exam is recognized globally as a standard of excellence in the field of machine learning engineering. It is a valuable credential that can enhance the career prospects of individuals by demonstrating their expertise and proficiency in machine learning engineering to potential employers.

Google Professional Machine Learning Engineer Certification Exam is a professional certification that tests the knowledge and skills of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is designed to evaluate the proficiency of candidates in various aspects of machine learning, including data processing, modeling, and deployment. Google Professional Machine Learning Engineer certification is offered by Google Cloud, a subsidiary of Google that provides cloud computing services to businesses and individuals.

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The Google Professional-Machine-Learning-Engineer exam comprises multiple-choice questions, performance-based tasks, and case studies that assess the candidate's ability to design and implement machine learning solutions using Google Cloud's machine learning tools and services. Professional-Machine-Learning-Engineer exam is designed to test the candidate's knowledge of key machine learning concepts, such as supervised and unsupervised learning, deep learning, natural language processing, and computer vision. Professional-Machine-Learning-Engineer Exam also evaluates the candidate's understanding of how to build scalable and reliable machine learning models that can handle large datasets.

Google Professional Machine Learning Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
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?

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

Answer: D

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 # 36
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

A. Compare the mean average precision across the models using the Continuous Evaluation feature
B. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
C. Compare the loss performance for each model on a held-out dataset.
D. Compare the loss performance for each model on the validation data

Answer: A

Explanation:
https://cloud.google.com/ai-platform/prediction/docs/continuous-evaluation/view-metrics

NEW QUESTION # 37
Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

A. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.
B. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.
C. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.
D. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.

Answer: A

Explanation:
A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors. The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to- date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models. References:
* Dataflow documentation
* RunInference API documentation
* Automatic model refresh documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 38
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:
* Option A is incorrect because using latitude, longitude, and product type as features, and using profit as model output is not the best way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option does not capture the interaction between latitude and longitude, which may affect the profitability of the product. For example, the same product may have different profitability in different regions, depending on the climate, culture, or preferences of the customers. Moreover, this option does not account for the granularity of the location data, which may be too fine or too coarse for the model. For example, using the exact coordinates of a city may not be meaningful, as the profitability may vary within the city, or using the country name may not be informative, as the profitability may vary across the country.
* Option B is incorrect because using latitude, longitude, and product type as features, and using revenue and expenses as model outputs is not a suitable way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option has the same drawbacks as option A, as it does not capture the interaction between latitude and longitude, or account for the granularity of the location data. Moreover, this option does not directly predict the profitability of the product, which is the target variable of interest. Instead, it predicts the revenue and expenses of the product, which are intermediate variables that depend on other factors, such as the price, the cost, or the demand of the product. To obtain the profitability, we would need to subtract the expenses from the revenue, which may introduce errors or uncertainties in the prediction.
* Option C is correct because using product type and the feature cross of latitude with longitude, followed by binning, as features, and using profit as model output is a good way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option captures the interaction between latitude and longitude, which may affect the profitability of the product, by creating a feature cross of these two features. A feature cross is a synthetic feature that combines the values of two or more features into a single feature1. This option also accounts for the granularity of the location data, by binning the feature cross into discrete buckets. Binning is a technique that groups continuous values into intervals, which can reduce the noise and complexity of the data2. Moreover, this option directly predicts the profitability of the product, which is the target variable of interest, by using it as the model output.
* Option D is incorrect because using product type and the feature cross of latitude with longitude, followed by binning, as features, and using revenue and expenses as model outputs is not a valid way to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations. This option has the same advantages as option C, as it captures the interaction between latitude and longitude, and accounts for the granularity of the location data, by creating a feature cross and binning it. However, this option does not directly predict the profitability of
* the product, which is the target variable of interest, but rather predicts the revenue and expenses of the product, which are intermediate variables that depend on other factors, as explained in option B.
References:
* Feature cross
* Binning
* [Profitability]
* [Revenue and expenses]
* [Latitude and longitude]
* [Product type]

NEW QUESTION # 39
You work for a manufacturing company. You need to train a custom image classification model to detect product detects at the end of an assembly line. Although your model is performing well, some images in your holdout set are consistently mislabeled with high confidence. You want to use Vertex Al to understand your models results. What should you do?

A. Configure example-based explanations. Specify the embedding output layer to be used for the latent space representation.
B. Configure feature-based explanations by using sampled Shapley. Set number of feature permutations to the maximum value of 50.
C. Configure example-based explanations by using integrated gradients. Set visualization type to pixels, and set clip_percent_upperbound to 95.
D. Create an index by using Vertex Al Matching Engine. Query the index with your mislabeled images

Answer: C

NEW QUESTION # 40
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Google Professional Machine Learning Engineer Sample Questions (Q35-Q40):

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