2023年最新の実際に出ると確認された 無料Google Professional-Machine-Learning-Engineer試験問題
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Google Machine-Machine-Learning-Enginer認定試験は、Google Cloudプラットフォームに機械学習モデルの構築と展開に関する専門知識を実証したいと考えている機械学習エンジニア、データサイエンティスト、およびソフトウェア開発者を対象としています。この試験では、データの準備と分析、機能エンジニアリング、モデルの選択とトレーニング、モデルの評価と最適化、Googleクラウドプラットフォームでの機械学習モデルの展開と管理など、幅広いトピックをカバーしています。
質問 # 27
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)
- A. AWS Health
- B. AWS Config
- C. AWS CloudTrail
- D. AWS Trusted Advisor
- E. Amazon CloudWatch
正解：C、E
解説：
Explanation/Reference: https://aws.amazon.com/sagemaker/faqs/
質問 # 28
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
- A. K-means
- B. Linear regression
- C. Principal component analysis (PCA)
- D. Logistic regression
正解：B
質問 # 29
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that feature expectations are captured in the schema
- B. Ensure that training is reproducible
- C. Ensure that model performance is monitored
- D. Ensure that all hyperparameters are tuned
正解：B
質問 # 30
You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?
- A. Manage all relational entities in the Hive Metastore.
- B. Store all ML metadata in Google Cloud's operations suite.
- C. Store your tf.logging data in BigQuery.
- D. Manage your ML workflows with Vertex ML Metadata.
正解：B
質問 # 31
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
- A. Use Vertex Al Platform for distributed training
- B. Create a cluster on Dataproc for training
- C. Create a Managed Instance Group with autoscaling
- D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
正解：A
解説：
AI platform also contains kubeflow pipelines. you don't need to set up infrastructure to use it. For D you need to set up a kubernetes cluster engine. The question asks us to minimize infrastructure overheard.
質問 # 32
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
- A. Develop a regression model using BigQuery ML.
- B. Train a regression model using AutoML Tables.
- C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
- D. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
正解：B
質問 # 33
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that feature expectations are captured in the schema
- B. Ensure that training is reproducible
- C. Ensure that model performance is monitored
- D. Ensure that all hyperparameters are tuned
正解：B
解説：
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf
質問 # 34
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?
- A. Too few layers in the model for capturing information
- B. Lack of model retraining
- C. Poor data quality
- D. Incorrect data split ratio during model training, evaluation, validation, and test
正解：D
質問 # 35
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
- A. One feature obtained as an element-wise product between latitude, longitude, and car type
- B. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
- C. Three individual features binned latitude, binned longitude, and one-hot encoded car type
- D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
正解：C
質問 # 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 loss performance for each model on a held-out dataset.
- C. Compare the loss performance for each model on the validation data
- D. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
正解：A
解説：
https://cloud.google.com/ai-platform/prediction/docs/continuous-evaluation/view-metrics
質問 # 37
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- B. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- C. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
- D. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
正解：D
質問 # 38
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. Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.
- B. Use the tf.distribute.Strategy API and run a distributed training job.
- C. Enable early stopping in your Vertex AI Training job.
- D. Increase the instance memory to 512 GB and increase the batch size.
正解：C
質問 # 39
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A. Create an AWS Glue crawler to infer duplicate accounts in the source data.
- B. Create a FindMatches machine learning transform in AWS Glue.
- C. Execute the built-in FindDuplicates Amazon Athena query.
- D. Search for duplicate accounts in the AWS Glue Data Catalog.
正解：B
解説：
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
質問 # 40
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
- B. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
- C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- D. Significantly increase the max_batch_size TensorFlow Serving parameter
正解：B
質問 # 41
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- A. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- C. Create a tf.data.Dataset.prefetch transformation
- D. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
正解：D
解説：
Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset(). tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.
https://cloud.google.com/architecture/ml-on-gcp-best-practices#store-image-video-audio-and-unstructured-data-on-cloud-storage
" Store image, video, audio and unstructured data on Cloud Storage Store these data in large container formats on Cloud Storage. This applies to sharded TFRecord files if you're using TensorFlow, or Avro files if you're using any other framework. Combine many individual images, videos, or audio clips into large files, as this will improve your read and write throughput to Cloud Storage. Aim for files of at least 100mb, and between 100 and 10,000 shards. To enable data management, use Cloud Storage buckets and directories to group the shards. "
質問 # 42
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