多くのGoogleのProfessional-Machine-Learning-Engineer認定試験を準備している受験生がいろいろなProfessional-Machine-Learning-Engineer「Google Professional Machine Learning Engineer」認証試験についてサービスを提供するサイトオンラインがみつけたがGoShikenはIT業界トップの専門家が研究した参考材料で権威性が高く、品質の高い教育資料で、一回に参加する受験者も合格するのを確保いたします。
認定試験は、機械学習の特定の側面に焦点を当てた複数のセクションに分かれています。これらのセクションには、データの準備、モデル構築、モデルの展開、監視が含まれています。各セクションは、個人が実践的な環境で機械学習の概念を適用する能力を試験するよう設計されています。試験形式には、多肢選択問題、ケーススタディ、実習演習が含まれており、個人の機械学習の概念を実際のシナリオに適用する能力を測定します。
Googleのプロフェッショナル機械学習エンジニア試験では、データ前処理、フィーチャーエンジニアリング、モデル構築、モデル展開、モデルモニタリング、外れ値検出、ハイパーパラメータ調整、アルゴリズム選択などへの実践的な経験が必要とされます。この認定は、実践的な経験を持ち、多才なプログラミングプロフェッショナルとして自己を認定するための知識と業界の認識を提供することを目的としています。
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Professional-Machine-Learning-Engineer最速合格、Professional-Machine-Learning-Engineer予想試験
当社のProfessional-Machine-Learning-Engineer試験資料は、この時代の製品であり、時代全体の開発動向に適合しています。覚えているので、私たちは勉強と試験の状態にあり、無数のテストを経験しているようです。就職活動の過程で、私たちは常に何が達成され、どのような証明書を取得したのかと尋ねられます。したがって、私たちはテストProfessional-Machine-Learning-Engineer認定を取得し、資格認定を取得して定量的標準になります。また、当社のProfessional-Machine-Learning-Engineer学習ガイドは、ごく短時間で最速を証明するのに役立ちます。
Google Machine-Machine-Learning-Enginer認定試験の資格を得るには、Google Cloudプラットフォームまたは同様のプラットフォームで機械学習モデルの開発と展開で少なくとも3年の経験が必要です。また、Python、Java、C ++などのプログラミング言語の経験があり、監視や教師のない学習、深い学習、強化学習などの機械学習の概念をよく理解する必要があります。
Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q50-Q55):
質問 # 50
A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?
- A. Copy the training program to directory /opt/ml/train
- B. Use CMD configin the Dockerfile to add the training program as a CMD of the image
- C. Configure the training program as an ENTRYPOINTnamed train
- D. Modify the bash_profile file in the container and add a bashcommand to start the training program
正解：B
質問 # 51
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?
- A. Use Dataflow with the SavedModel to read the data from BigQuery
- B. Deploy and version the model on Al Platform.
- C. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
- D. Export the model to BigQuery ML.
正解：D
質問 # 52
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
- A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- B. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- C. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- D. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
正解：C
解説：
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture
質問 # 53
You work at a large organization that recently decided to move their ML and data workloads to Google Cloud.
The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction How should you configure the pipeline?
- A. Ingest the Avro files into BigQuery to perform analytics Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.
- B. Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features. and store them in Vertex Al Feature Store for online prediction.
- C. Ingest the Avro files into BigQuery to perform analytics Use a Dataflow pipeline to create the features, and store them in Vertex Al Feature Store for online prediction.
- D. Ingest the Avro files into Cloud Spanner to perform analytics Use a Dataflow pipeline to create the features and store them in BigQuery for online prediction.
正解：C
解説：
BigQuery is a service that allows you to store and query large amounts of data in a scalable and cost-effective way. You can use BigQuery to ingest the Avro files from the Cloud Storage bucket and perform analytics on the structured data. Avro is a binary file format that can store complex data types and schemas. You can use the bq load command or the BigQuery API to load the Avro files into a BigQuery table. You can then use SQL queries to analyze the data and generate insights. Dataflow is a service that allows you to create and run scalable and portable data processing pipelines on Google Cloud. You can use Dataflow to create the features for your ML models, such as transforming, aggregating, and encoding the data. You can use the Apache Beam SDK to write your Dataflow pipeline code in Python or Java. You can also use the built-in transforms or custom transforms to apply the feature engineering logic to your data. Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to host the features that your ML models use for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can use the Vertex AI Feature Store API to write the features from your Dataflow pipeline to a feature store entity type. You can then use the Vertex AI Feature Store online serving API to read the features from the feature store and pass them to your ML models for online prediction. By using BigQuery, Dataflow, and Vertex AI Feature Store, you can configure a pipeline that performs analytics, creates features, and hosts the features that your ML models use for online prediction. References:
* BigQuery documentation
* Dataflow documentation
* Vertex AI Feature Store documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
質問 # 54
You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?
- A. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
- B. Containerize the PySpark transformation step, and add it to your pipeline.
- C. Remove the data transformation step from your pipeline.
- D. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
正解：A
解説：
The best option for parametrizing the model training in Kubeflow Pipelines is to add a ContainerOp to the pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage. This option has the following advantages:
* It allows the data transformation to be performed as part of the Kubeflow Pipeline, which can ensure the consistency and reproducibility of the data processing and the model training. By adding a ContainerOp to the pipeline, you can define the parameters and the logic of the data transformation step, and integrate it with the other steps of the pipeline, such as the model training and evaluation.
* It leverages the scalability and performance of Dataproc, which is a fully managed service that runs Apache Spark and Apache Hadoop clusters on Google Cloud. By spinning a Dataproc cluster, you can run the PySpark transformation on the Parquet files stored in the Hive table, and take advantage of the parallelism and speed of Spark. Dataproc also supports various features and integrations, such as autoscaling, preemptible VMs, and connectors to other Google Cloud services, that can optimize the data processing and reduce the cost.
* It simplifies the data storage and access, as the transformed data is saved in Cloud Storage, which is a scalable, durable, and secure object storage service. By saving the transformed data in Cloud Storage, you can avoid the overhead and complexity of managing the data in the Hive table or the Parquet files.
Moreover, you can easily access the transformed data from Cloud Storage, using various tools and frameworks, such as TensorFlow, BigQuery, or Vertex AI.
The other options are less optimal for the following reasons:
* Option A: Removing the data transformation step from the pipeline eliminates the parametrization of the
* model training, as the data processing and the model training are decoupled and independent. This option requires running the PySpark transformation separately from the Kubeflow Pipeline, which can introduce inconsistency and unreproducibility in the data processing and the model training. Moreover, this option requires managing the data in the Hive table or the Parquet files, which can be cumbersome and inefficient.
* Option B: Containerizing the PySpark transformation step, and adding it to the pipeline introduces additional complexity and overhead. This option requires creating and maintaining a Docker image that can run the PySpark transformation, which can be challenging and time-consuming. Moreover, this option requires running the PySpark transformation on a single container, which can be slow and inefficient, as it does not leverage the parallelism and performance of Spark.
* Option D: Deploying Apache Spark at a separate node pool in a Google Kubernetes Engine cluster, and adding a ContainerOp to the pipeline that invokes a corresponding transformation job for this Spark instance introduces additional complexity and cost. This option requires creating and managing a separate node pool in a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires deploying and running Apache Spark on the node pool, which can be tedious and costly, as it requires configuring and maintaining the Spark cluster, and paying for the node pool usage.
質問 # 55
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Professional-Machine-Learning-Engineer最速合格: https://www.goshiken.com/Google/Professional-Machine-Learning-Engineer-mondaishu.html