Professional-Machine-Learning-Engineer模擬試験問題集 & Professional-Machine-Learning-Engineer日本語関連対策

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Google Professional-Machine-Learning-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Machine Learning Engineer Certification Exam
Exam Number:	Professional-Machine-Learning-Engineer
Related Certifications:	Google Cloud Professional Data Engineer Google Cloud Professional Cloud Architect
Real Exam Qty:	50-60
Exam Price:	$200 USD (plus tax where applicable)
Exam Duration:	120 minutes
Exam Format:	Multiple select, Multiple choice
Available Languages:	Japanese, English
Passing Score:	Not officially published, approximately 70%
Certificate Validity Period:	2 years
Recommended Training:	Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path Official Exam Guide
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Machine-Learning-Engineer Sample Questions
Exam Way:	Online-proctored remote exam or onsite-proctored exam at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/machine-learning-engineer

>> Professional-Machine-Learning-Engineer模擬試験問題集 <<

Google Professional-Machine-Learning-Engineer日本語関連対策、Professional-Machine-Learning-Engineer日本語問題集

Professional-Machine-Learning-Engineer実践教材は、すべての点で同様の製品よりも優れていると自信を持って伝えることができます。まず、ユーザーはProfessional-Machine-Learning-Engineer試験準備を無料で試用して、Professional-Machine-Learning-Engineerスタディガイドをよりよく理解することができます。ユーザーが製品が自分に適していないことに気付いた場合、ユーザーは別の種類の学習教材を選択できます。ユーザーの選択を尊重し、ユーザーがProfessional-Machine-Learning-Engineer実践教材を購入する必要があることを強制しません。ユーザーが適格なProfessional-Machine-Learning-Engineer試験に合格できるように、ユーザーのすべての要件を可能な限り満たすことができます。

Google Professional Machine Learning Engineer認定資格を取得することは、Google Cloudの技術を使用して機械学習モデルを設計および実装する能力を証明し、キャリアアップの機会と就職先の増加につながることがあります。これは、機械学習の分野で高く評価され、世界中の業界専門家に認められた資格です。

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q207-Q212):

質問 # 207
As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A. Use the batch prediction functionality of AI Platform.
B. Use Cloud Functions for prediction each time a new data point is ingested.
C. Create a serving pipeline in Compute Engine for prediction.
D. Deploy the model on AI Platform and create a version of it for online inference.

正解：A

解説：
You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention.

質問 # 208
You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline. The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform. How should you configure the architecture before deploying the model to production?

A. Create a new AI Platform model version - > Deploy model in test environment -> Validate model
B. Deploy model in test environment -> Evaluate and test model -> Create a new AI Platform model version
C. Validate model -> Deploy model in test environment -> Create a new AI Platform model version
D. Create a new AI Platform model version -> Evaluate and test model -> Deploy model in test environment

正解：B

解説：
A is correct because the model can be validated after it is deployed to the test environment, and the release version is established before the model is deployed in production.
B is not correct because the model cannot be validated before being deployed to the test environment.
C is not correct because the model version is being set up for the release candidate before the model is validated. Moreover, the model cannot be validated before being deployed to the test environment.
D is not correct because the model version is being set up for the release candidate before the model is validated.
https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build

質問 # 209
You are a data analyst on a marketing team. You have 40 GB of customer transaction data already stored in BigQuery, and you need to produce a churn-propensity model quickly to validate whether the project is worth further investment. Your team has strong SQL skills but limited Python experience. You want to minimize infrastructure setup. What should you do?

A. Build an AutoML tabular training job in Agent Platform by importing the BigQuery table as a managed dataset.
B. Create a BigQuery ML logistic regression model directly on the transaction table, and evaluate it with ML.EVALUATE.
C. Deploy a Dataproc cluster, and use Spark MLlib to train a gradient-boosted tree classifier.
D. Export the data to Cloud Storage, and train a scikit-learn model in a Workbench instance using a custom training job.

正解：B

解説：
BigQuery ML lets the team train and evaluate a model with SQL where the data already resides, avoiding data movement and infrastructure provisioning. For a fast feasibility check with SQL- oriented staff, it delivers a baseline model with the least effort, and the model can later be exported or promoted to a managed service if the project proceeds.

質問 # 210
You are optimizing the training of a 175-billion parameter LLM on Gemini Enterprise Agent Platform. You have provisioned a TPU v5p Pod slice with 32 chips for the job. You initially ran the training using standard data parallelism, but the job immediately failed with an out-of-memory (OOM) error. You need to implement a training strategy that resolves the memory issue and minimizes training latency. What should you do?

A. Configure the training to shard the model parameters and optimizer states, and continue using the data parallelism strategy.
B. Enable gradient checkpointing, and continue using the data parallelism strategy.
C. Configure the training to use a parameter server architecture where the model weights are stored in the host CPU memory.
D. Scale up the TPU Pod slice to include more chips, and continue using the data parallelism strategy.

正解：A

解説：
Sharding the model parameters and optimizer states across the TPU chips distributes the memory footprint instead of replicating the full 175-billion-parameter model on every device. This resolves the out-of-memory failure while retaining data parallelism for high-throughput training and lower overall training latency.

質問 # 211
You work in the credit card fraud detection department of a bank that processes millions of transactions per day. You need to create and serve a real-time fraud detection model. To address the challenge of feature engineering at this scale with limited raw data, you plan to create time- related aggregate features, such as the average transaction amount within the last month or the amount of time since the last transaction. How should you process the data for model training and serving?

A. Ingest the transaction data into BigQuery. Use a TRANSFORM_ONLY model in BigQuery ML to create a reusable feature engineering pipeline. Use this pipeline to preprocess the data.
B. Create streaming Dataflow pipelines that read transaction data from Pub/Sub. Use a windowing function to aggregate transaction data over time. Store the engineered features in Agent Platform Feature Store.
C. Ingest the transaction data into BigQuery. Use the TRANSFORM clause in BigQuery ML to create new features by applying SQL functions to the raw transaction data.
D. Orchestrate a workflow in Agent Platform Pipelines that exports raw transaction data from BigQuery to Cloud Storage. Perform feature engineering using a Python script in a custom component in the pipeline.

正解：B

解説：
A streaming Dataflow pipeline can process millions of transaction events from Pub/Sub and use stateful, windowed aggregations to calculate time-dependent features such as monthly average transaction amounts and time since the previous transaction. Storing these engineered values in Agent Platform Feature Store makes consistent features available for both model training and low-latency online serving.
Reference:
https://docs.cloud.google.com/dataflow/docs/concepts/streaming-pipelines
https://docs.cloud.google.com/dataflow/docs/concepts/streaming-with-cloud-pubsub

質問 # 212
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