P.S. Tech4ExamがGoogle Driveで共有している無料かつ新しいProfessional-Machine-Learning-Engineerダンプ：https://drive.google.com/open?id=1x6LyuFTUnSm79lw1z0EN780TlIEqfVcL
Google Professional-Machine-Learning-Engineer試験を目前に控えて、不安なのですか。我々社のGoogle Professional-Machine-Learning-Engineer問題集のソフト版を購買するに値するかまだ疑問がありますか。こうしたら、我々Tech4ExamのProfessional-Machine-Learning-Engineer問題集デーモを無料にダウンロードして行動してみよう。我々提供するProfessional-Machine-Learning-Engineer試験資料はあなたの需要を満足できると知られています。我々にとって、Google Professional-Machine-Learning-Engineer試験に参加する圧力を減らして備考効率を高めるのは大変名誉のことです。
この認定を取得するには、候補者は機械学習とクラウドコンピューティングに関連する幅広いトピックをカバーする厳格な試験に合格する必要があります。試験は、複数選択とシナリオベースの質問で構成されており、候補者は試験を完了するために2時間半が与えられます。試験はオンラインで管理されており、世界中のどこからでも取得できます。試験に合格すると、候補者はLinkedInプロファイル、履歴書、またはWebサイトに表示できるデジタルバッジを受け取り、機械学習の分野とGoogle Cloudプラットフォームの習熟度を示していることを示します。この認定は、業界の専門家によって認識され、個人が機械学習とクラウドコンピューティングの分野でキャリアを前進させるのに役立ちます。
>> Professional-Machine-Learning-Engineer模擬モード <<
ユーザーのニーズによりよく応えるために、Professional-Machine-Learning-Engineer調査の質問では、ユーザーがプロのワンストップサービスを利用できるように、サービスシステムの完全なセットを設定しました。ユーザー向けのプレセールで無料デモを提供するだけでなく、ユーザーが購入できる3つのバージョンを選択できると同時に、Professional-Machine-Learning-Engineerトレーニング資料も24時間のアフターサービスを提供します。私たちのProfessional-Machine-Learning-Engineerテストガイドの完璧なワンストップサービスは、あなたが選択を後悔することはないと信じており、あなたの時間、完全な勉強、効率的にProfessional-Machine-Learning-Engineer試験に合格することができると信じています。
Google Professional Machine Learning Engineer認定試験では、候補者が監督者や監督なしの学習、深い学習、自然言語処理、コンピュータービジョンなどの機械学習の概念を強く理解する必要があります。この試験では、Apache Spark、Apache Hadoop、Google Bigqueryなどのビッグデータテクノロジーを使用する候補者の能力もテストしています。
人工知能は、個人用バーチャルアシスタントから自動運転車まで、私たちが知っている世界を大きく形作っています。そのため、Googleのプロフェッショナル機械学習エンジニア認定を持つことは、高収入の魅力的なキャリアパスの提供を促し、産業の認識と能力を証明するものとなります。この認定は、MLモデルの設計、開発、プロダクション化、モニタリング、およびチーム間の調整を可能にする能力を認め、業界の知識と認識を提供します。
質問 # 134
You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?
正解：B
解説：
To create a Vertex AI Workbench environment for your team and limit access to other employees in your project, you should follow these steps:
* Create a new service account and grant it the Vertex AI User role. This role grants full access to all resources in Vertex AI, including creating and managing notebook instances1.
* Grant the Service Account User role to each team member on the service account. This role allows the team members to impersonate the service account and use its permissions2.
* Grant the Notebook Viewer role to each team member. This role allows the team members to view and
* connect to the notebook instance, but not to modify or delete it3.
* Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account.
This way, the notebook instance will run as the service account and only the team members who have the Service Account User and Notebook Viewer roles will be able to access it.
References:
* 1: Vertex AI access control with IAM | Google Cloud
* 2: Understanding service accounts | Cloud IAM Documentation
* 3: Manage access to a Vertex AI Workbench instance | Google Cloud
* [4]: Create and manage Vertex AI Workbench instances | Google Cloud
質問 # 135
You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?
正解：D
解説：
Vertex AI batch prediction is the most appropriate and efficient way to apply a pre-trained model like TensorFlow's SavedModel to a large dataset, especially for batch processing.
The Vertex AI batch prediction job works by exporting your dataset (in this case, historical data from BigQuery) to a suitable format (like Avro or CSV) and then processing it in Cloud Storage where the model is stored.
Avro format is recommended for large datasets as it is highly efficient for data storage and is optimized for read/write operations in Google Cloud, which is why option B is correct.
Option A suggests using BigQuery ML for inference, but it does not support running arbitrary TensorFlow models directly within BigQuery ML. Hence, BigQuery ML is not a valid option for this particular task.
Option C (exporting to CSV) is a valid alternative but is less efficient compared to Avro in terms of performance.
Option D suggests deploying a Vertex AI endpoint, which is better suited for real-time inference rather than batch inference. Since the question asks for batch inference, B is the best answer.
質問 # 136
You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table How should you perform the inference?
正解：D
解説：
The best option for implementing a batch inference ML pipeline in Google Cloud, using a model that was developed using TensorFlow and is stored in SavedModel format in Cloud Storage, and a historical dataset containing 10 TB of data that is stored in a BigQuery table, is to configure a Vertex AI batch prediction job to apply the model to the historical data in BigQuery. This option allows you to leverage the power and simplicity of Vertex AI and BigQuery to perform large-scale batch inference with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can run a batch prediction job, which can generate predictions for a large number of instances in batches. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A batch prediction job is a resource that can run your model code on Vertex AI. A batch prediction job can help you generate predictions for a large number of instances in batches, and store the prediction results in a destination of your choice. A batch prediction job can accept various input formats, such as JSON, CSV, or TFRecord. A batch prediction job can also accept various input sources, such as Cloud Storage or BigQuery. A TensorFlow model is a resource that represents a machine learning model that is built using TensorFlow. TensorFlow is a framework that can perform large-scale data processing and machine learning. TensorFlow can help you build and train various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. A SavedModel format is a type of format that can store a TensorFlow model and its associated assets. A SavedModel format can help you save and load your TensorFlow model, and serve it for prediction. A SavedModel format can be stored in Cloud Storage, which is a service that can store and access large-scale data on Google Cloud. A historical dataset is a collection of data that contains historical information about a certain domain. A historical dataset can help you analyze the past trends and patterns of the data, and make predictions for the future. A historical dataset can be stored in BigQuery, which is a service that can store and query large-scale data on Google Cloud. BigQuery can help you analyze your data by using SQL queries, and perform various tasks, such as data exploration, data transformation, or data visualization. By configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, you can implement a batch inference ML pipeline in Google Cloud with minimal code and configuration. You can use the Vertex AI API or the gcloud command-line tool to configure a batch prediction job, and provide the model name, the model version, the input source, the input format, the output destination, and the output format. Vertex AI will automatically run the batch prediction job, and apply the model to the historical data in BigQuery. Vertex AI will also store the prediction results in a destination of your choice, such as Cloud Storage or BigQuery1.
The other options are not as good as option D, for the following reasons:
* Option A: Exporting the historical data to Cloud Storage in Avro format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. Avro is a type of format that can store and serialize data in a binary format. Avro can help you compress and encode your data, and support schema evolution and compatibility. By exporting the historical data to Cloud Storage in Avro format, configuring a Vertex AI batch prediction job to generate predictions for the exported data, you can perform batch inference with minimal code and configuration. You can use the BigQuery API or the bq command-line tool to export the historical data to Cloud Storage in Avro format, and use the Vertex AI API or the gcloud command-line tool to configure a batch prediction job, and provide the model name, the model version, the input source, the input format, the output destination, and the output format. However, exporting the historical data to Cloud Storage in Avro format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. You would need to write code, export the historical data to Cloud Storage, configure a batch prediction job, and generate predictions for the exported data. Moreover, this option would not use BigQuery as the input source for the batch prediction job, which can simplify the batch inference process, and provide various benefits, such as fast query performance, serverless scaling, and cost optimization2.
* Option B: Importing the TensorFlow model by using the create model statement in BigQuery ML, applying the historical data to the TensorFlow model would not allow you to use Vertex AI to run the batch prediction job, and could increase the complexity and cost of the batch inference process.
BigQuery ML is a feature of BigQuery that can create and execute machine learning models in BigQuery by using SQL queries. BigQuery ML can help you build and train various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. A create model statement is a type of SQL statement that can create a machine learning model in BigQuery ML. A create model statement can help you specify the model name, the model type, the model options, and the model query. By importing the TensorFlow model by using the create model statement in BigQuery ML, applying the historical data to the TensorFlow model, you can perform batch inference with minimal code and configuration. You can use the BigQuery API or the bq command-line tool to import the TensorFlow model by using the create model statement in BigQuery ML, and provide the model name, the model type, the model options, and the model query. You can also use the BigQuery API or the bq command-line tool to apply the historical data to the TensorFlow model, and provide the model name, the input data, and the output destination. However, importing the TensorFlow model by using the create model statement in BigQuery ML, applying the historical data to the TensorFlow model would not allow you to use Vertex AI to run the batch prediction job, and could increase the complexity and cost of the batch inference process. You would need to write code, import the TensorFlow model, apply the historical data, and generate predictions. Moreover, this option would not use Vertex AI, which is a unified platform for building and deploying machine learning solutions on Google Cloud, and provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance3.
* Option C: Exporting the historical data to Cloud Storage in CSV format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. CSV is a type of format that can store and serialize data in a comma-separated values format. CSV can help you store and exchange your data, and support various data types and formats. By exporting the historical data to Cloud Storage in CSV format, configuring a Vertex AI batch prediction job to generate predictions for the exported data, you can perform batch inference with minimal code and configuration. You can use the BigQuery API or the bq command-line tool to export the historical data to Cloud Storage in CSV format, and use the Vertex AI API or the gcloud command-line tool to configure a batch prediction job, and provide the model name, the model version, the input source, the input format, the output destination, and the output format. However, exporting the historical data to Cloud Storage in CSV format, configuring a Vertex AI batch prediction job to generate predictions for the exported data would require more skills and steps than configuring a Vertex AI batch prediction job to apply the model to the historical data in BigQuery, and could increase the complexity and cost of the batch inference process. You would need to write code, export the historical data to Cloud Storage, configure a batch prediction job, and generate predictions for the exported data. Moreover, this option would not use BigQuery as the input source for the batch prediction job, which can simplify the batch inference process, and provide various benefits, such as fast query performance, serverless scaling, and cost optimization2.
References:
* Batch prediction | Vertex AI | Google Cloud
* Exporting table data | BigQuery | Google Cloud
* Creating and using models | BigQuery ML | Google Cloud
質問 # 137
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
正解：B
解説：
AI Platform Training is a service that allows you to run your machine learning experiments on Google Cloud using various features, model architectures, and hyperparameters. You can use AI Platform Training to scale up your experiments, leverage distributed training, and access specialized hardware such as GPUs and TPUs 1
. Cloud Monitoring is a service that collects and analyzes metrics, logs, and traces from Google Cloud, AWS, and other sources. You can use Cloud Monitoring to create dashboards, alerts, and reports based on your data
2 . The Monitoring API is an interface that allows you to programmatically access and manipulate your monitoring data 3 .
By using AI Platform Training and Cloud Monitoring, you can track and report your experiments while minimizing manual effort. You can write the accuracy metrics from your experiments to Cloud Monitoring usin g the AI Platform Training Python package 4 . You can then query the results using the Monitoring API and compare the performance of different experiments. You can also visualize the metrics in the Cloud Console or create custom dashboards and alerts 5 . Therefore, using AI Platform Training and Cloud Monitoring is the best option for this use case.
References:
AI Platform Training documentation
Cloud Monitoring documentation
Monitoring API overview
Using Cloud Monitoring with AI Platform Training
Viewing evaluation metrics
質問 # 138
You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?
正解：C
質問 # 139
......
Professional-Machine-Learning-Engineer資格取得: https://www.tech4exam.com/Professional-Machine-Learning-Engineer-pass-shiken.html
ちなみに、Tech4Exam Professional-Machine-Learning-Engineerの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1x6LyuFTUnSm79lw1z0EN780TlIEqfVcL