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2. Google Professional-Machine-Learning- Engineer Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 Q147): Google Professional Machine Learning Engineer 2 認定 試験問題 (Q142- 質問 # 142 質問 # 142 A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC. Why is the ML Specialist not seeing the instance visible in the VPC? pass4test.jp A. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts. B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts. C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts. D. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs. 正解 正解：C 解説： Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/gs-setup-working-env.html 質問 # 143 質問 # 143 A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce. Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.) A. Numerical value binning B. High-degree polynomial transformation C. One hot encoding D. Logarithmic transformation E. Cross-validation 正解 正解：A、E 質問 # 144 質問 # 144 While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the なProfessional-Machine-Learning-Engineer試験感想一回合格-一番優秀 なProfessional-Machine-Learning-Engineer試験対策 信頼的 信頼的な 試験感想一回合格-一番優秀な 試験対策

3. Google Professional-Machine-Learning- Engineer execution time of your input pipeline. What should you do? Google Professional Machine Learning Engineer 3 A. Add parallel interleave to the pipeline B. Increase the network bandwidth C. Increase the CPU load D. Add caching to the pipeline 正解 正解：C 質問 # 145 質問 # 145 You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company's catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event dat a. How should you build the recommendation system for the first version of the product? pass4test.jp A. Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future. B. Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data. C. Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster. D. Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future. 正解 正解：B 質問 # 146 質問 # 146 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 AutoML Natural Language to extract custom entities for classification B. Use the Al Platform Training built-in algorithms to create a custom model C. Use the Cloud Natural Language API to extract custom entities for classification D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm 正解 正解：B 質問 # 147 質問 # 147 なProfessional-Machine-Learning-Engineer試験感想一回合格-一番優秀 なProfessional-Machine-Learning-Engineer試験対策 信頼的 信頼的な 試験感想一回合格-一番優秀な 試験対策

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