Professional-Machine-Learning-Engineer英語版 & Professional-Machine-Learning-Engineer模擬試験問題集

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一部のお客様は時間を無駄にしないホワイトカラーの従業員であり、プロモーションを得るために早急にGoogle認定を必要としますが、他のお客様はスキルの向上を目指している場合があります。そのため、Professional-Machine-Learning-Engineerの質問と回答の異なるバージョンを設定することにより、異なる要件を満たすようにします。特別なものは、オンラインのProfessional-Machine-Learning-Engineerエンジンバージョンです。オンラインツールとして、便利で簡単に学習でき、Windows、Mac、Android、iOSなどを含むすべてのWebブラウザとシステムをサポートします。このバージョンのProfessional-Machine-Learning-Engineer試験問題をすべての電子デバイスに適用できます。

Google Professional Machine Learning Engineer Examは、機械学習エンジニアリングにおける個人の専門知識を検証するために設計された認定試験です。この試験の目的は、Google Cloud Platform Technologiesを使用して、高度にスケーラブルで堅牢で保守可能な機械学習モデルを作成および展開する候補者の能力を評価することを目的としています。この試験では、機械学習アーキテクチャの設計と実装、機械学習を使用したビジネス上の問題の解決、および機械学習ワークフローの最適化における候補者の習熟度もテストしています。

Google Professional Machine Learning Engineer認定は、業界で非常に尊敬されており、機械学習の卓越性のベンチマークとして認識されています。この認定を達成することは、雇用主と仲間に、候補者がGoogleクラウドプラットフォームに機械学習モデルを設計、構築、展開するために必要なスキルと知識を持っていることを示しています。この認定は、データサイエンティスト、機械学習エンジニア、ソフトウェアエンジニア、および機械学習のスキルを高め、この分野でのキャリアを促進しようとしている他の専門家に最適です。

Google Professional Machine Learning Engineer認定を取得することは、この分野の専門家にとって多数の利点を提供することができます。これにより、潜在的な雇用主やクライアントに自分の専門知識を証明することができ、競争優位性を得ることができます。さらに、機械学習エンジニアリングの分野でキャリアを発展させ、収益性を高めることができます。全体的に、Google Professional Machine Learning Engineer認定試験は、この急速に成長する分野でのスキルと知識を検証するための優れた機会です。

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q140-Q145):

質問 # 140
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

正解：D、E

解説：
The tf.data dataset is a TensorFlow API that provides a way to create and manipulate data pipelines for machine learning. The tf.data dataset allows you to apply various transformations to the data, such as reading, shuffling, batching, prefetching, and interleaving. The se transformations can affect the performance and efficiency of the model training process 1 One of the common performance issues in model training is input-bound, which means that the model is waiting for the input data to be ready and is not fully utilizing the computational resources. Input-bound can be caused by slow data loading, insufficient parallelism, or large data size. Input-bound can be detected by using the Cloud TPU profiler plugin, which is a tool that helps you analyze the performance of your model on Cloud TPUs. The Clo ud TPU profiler plugin can show you the percentage of time that the TPU cores are idle, which indicates input-bound 2 To reduce the input-bound bottleneck and speed up the model training process, you can make some modifications to the tf.data dataset. Two of the modifications that can help are:
* Use the interleave option for reading data. The interleave option allows you to read data from multiple files in parallel and interleave their records. This can improve the data loading speed and reduce the idle time of the TPU cores. The interleave option can be applied by using the tf.data.Dataset.
interleave method, which takes a function that returns a dataset for each input element, and a number of parallel calls 3
* Set the prefetch option equal to the training batch size. The prefetch option allows you to prefetch the next batch of data while the current batch is being processed by the model. This can reduce the latency between batches and improve the throughput of the model training. The prefetch option can be applied by using the tf.data.Dataset.prefetch method, which takes a buffer size argument. The buffer size should be equal to t he training batch size, which is the number of examples per batch 4 The other options are not effective or counterproductive. Reducing the value of the repeat parameter will reduce the number of epochs, which is the number of times the model sees the entire dataset. This can affect the model's accuracy and convergence. Increasing the buffer size for the shuffle option will increase the randomness of the data, but also increase the memory usage and the data loading time. Decreasing the batch size argument in your transformation will reduce the number of examples per batch, which can affect the model's stability and performance.
References: 1 : tf.data: Build TensorFlow input pipelines 2 : Cloud TPU Tools in TensorBoard 3 : tf.
data.D ataset.interleave 4 : tf.data.Dataset.prefetch : [Better performance with the tf.data API]


質問 # 141
You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

正解：C


質問 # 142
You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

正解：D

解説：
TFX (TensorFlow Extended) is a platform for end-to-end machine learning pipelines. It provides components for data ingestion, preprocessing, validation, model training, serving, and monitoring. Dataflow is a fully managed service for scalable data processing. By using TFX components with Dataflow, you can perform feature engineering on large-scale tabular data in a distributed and efficient way. You can use the Transform component to apply the MaxMin scaler and the one-hot encoding to the numerical and categorical features, respectively. You can also use the ExampleGen component to read data from BigQuery and the Trainer component to train your TensorFlow model. The output of the Transform component is a TFRecord file, which is a binary format for storing TensorFlow data. You can export the TFRecord file to Cloud Storage and feed it into Vertex AI Training, which is a managed service for training custom machine learning models on Google Cloud. References:
* TFX | TensorFlow
* Dataflow | Google Cloud
* Vertex AI Training | Google Cloud


質問 # 143
You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex Al Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?

正解：B

解説：
The best option to minimize storage and computational overhead is to use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics. The TRANSFORM clause allows you to specify feature preprocessing logic that applies to both training and prediction. The preprocessing logic is executed in the same query as the model creation, which avoids the need to create and store intermediate tables. The TRANSFORM clause also supports quantile bucketization and MinMax scaling, which are the preprocessing steps required for this scenario. Option A is incorrect because creating a component in the Vertex AI Pipelines DAG to calculate the required statistics may increase the computational overhead, as the component needs to run separately from the model creation. Moreover, the component needs to pass the statistics to subsequent components, which may increase the storage overhead. Option B is incorrect because preprocessing and staging the data in BigQuery prior to feeding it to the model may also increase the storage and computational overhead, as you need to create and maintain additional tables for the preprocessed data. Moreover, you need to ensure that the preprocessing logic is consistent for both training and inference. Option C is incorrect because creating SQL queries to calculate and store the required statistics in separate BigQuery tables may also increase the storage and computational overhead, as you need to create and maintain additional tables for the statistics. Moreover, you need to ensure that the statistics are updated regularly to reflect the new data. Reference:
BigQuery ML documentation
Using the TRANSFORM clause
Feature preprocessing with BigQuery ML


質問 # 144
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

正解：D

解説：
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html


質問 # 145
......

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