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Professional-Machine-Learning-Engineer Exam Introduction | Professional-Machine-Learning-Engineer Valid Test Voucher

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The Google Professional-Machine-Learning-Engineer Certification Exam is one of the top-rated career advancement certifications in the market. With the Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer certification exam everyone can validate their skills and knowledge after passing the Professional-Machine-Learning-Engineer text. The Google Google Professional Machine Learning Engineer certification exam will recognize your expertise and knowledge in the market. You will get solid proof of your proven skill set. There are other countless benefits that you can gain after passing the Google Google Professional Machine Learning Engineer certification exam.

Google Professional Machine Learning Engineer exam is a certification test that is designed to validate the skills and knowledge of individuals in the field of machine learning. Professional-Machine-Learning-Engineer Exam is intended for individuals who have a strong understanding of machine learning concepts, including supervised learning, unsupervised learning, and deep learning. Additionally, this certification exam assesses an individual's ability to design and implement machine learning models on the Google Cloud Platform.

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Google Professional Machine Learning Engineer Sample Questions (Q276-Q281):

NEW QUESTION # 276
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 = Al Platform, 3 = Cloud Storage
B. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
C. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
D. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

Answer: B

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 # 277
A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?

A. RecordIO
B. Compressed JSON
C. Parquet files
D. CSV files

Answer: C

NEW QUESTION # 278
You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

A. 1. Specify sampled Shapley as the explanation method with a path count of 50.
2. Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
B. 1 Specify Integrated Gradients as the explanation method with a path count of 50.
2. Deploy the model to Vertex Al Endpoints.
3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
C. 1 Specify sampled Shapley as the explanation method with a path count of 5.
2 Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
D. 1 Specify Integrated Gradients as the explanation method with a path count of 5.
2 Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

Answer: C

Explanation:
Sampled Shapley is a fast and scalable approximation of the Shapley value, which is a game-theoretic concept that measures the contribution of each feature to the model prediction. Sampled Shapley is suitable for online prediction requests, as it can return feature attributions with minimal latency. The path count parameter controls the number of samples used to estimate the Shapley value, and a lower value means faster computation. Integrated Gradients is another explanation method that computes the average gradient along the path from a baseline input to the actual input. Integrated Gradients is more accurate than Sampled Shapley, but also more computationally intensive. Therefore, it is not recommended for online prediction requests, especially with a high path count. Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model. Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal.
Training-serving skew is the difference between the data used for training the model and the data used for serving the model. It can also affect the performance and accuracy of the model, and may indicate data quality issues or model staleness. Vertex AI Model Monitoring allows you to monitor training-serving skew on your deployed models and endpoints, and set up alerts and notifications when the skew exceeds a certain threshold.
However, this is not relevant to the question, as the question is about the feature attributions of the model, not the data distribution. References:
* Vertex AI: Explanation methods
* Vertex AI: Configuring explanations
* Vertex AI: Monitoring prediction drift
* Vertex AI: Monitoring training-serving skew

NEW QUESTION # 279
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

A. Address the model overfitting by using a less complex algorithm.
B. Address data leakage by applying nested cross-validation during model training.
C. Address data leakage by removing features highly correlated with the target value.
D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Answer: B

Explanation:
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9

NEW QUESTION # 280
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

A. Use the Al Platform Training built-in algorithms to create a custom model
B. Use AutoML Natural Language to extract custom entities for classification
C. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
D. Use the Cloud Natural Language API to extract custom entities for classification

Answer: A

NEW QUESTION # 281
......

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