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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Serving and scaling models	- Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Batch prediction
Topic 2: Architecting low-code ML solutions	- Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models - AutoML capabilities and implementation
Topic 3: Monitoring ML solutions	- Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies
Topic 4: Scaling prototypes into ML models	- Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
Topic 5: Automating and orchestrating ML pipelines	- CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines)
Topic 6: Collaborating within and across teams to manage data and models	- Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps)

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Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q58-Q63):

質問 # 58
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
C. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

正解：B

質問 # 59
Your team is developing a customer support chatbot for a healthcare company that processes sensitive patient information. You need to ensure that all personally identifiable information (PII) captured during customer conversations is protected prior to storing or analyzing the data. What should you do?

A. Use the Cloud Natural Language API to identify and redact PII in chatbot conversations.
B. Use the DLP API to encrypt PII in chatbot conversations before storing the data.
C. Use the Cloud Natural Language API to classify and categorize all data, including PII, in chatbot conversations.
D. Use the DLP API to scan and de-identify PII in chatbot conversations before storing the data.

正解：D

解説：
DLP API (Data Loss Prevention API) is specifically designed to identify, classify, and protect sensitive information like PII. It can scan chatbot conversations for PII and perform de- identification operations such as masking, tokenization, or redaction to ensure data privacy.
De-identifying PII before storage ensures compliance with data protection regulations such as HIPAA (for healthcare data) and enhances data security by removing or obfuscating sensitive information.

質問 # 60
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

A. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
B. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket
C. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.
E. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket

正解：C

質問 # 61
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. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
C. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
D. 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

正解：C

解説：
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2 To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3 The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45 References: 1: Cloud Build overview 2: Creating and managing build triggers 3: Building and deploying Kubeflow Pipelines using Cloud Build 4: Cloud Run documentation 5: Cloud Functions documentation

質問 # 62
You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do?

A. Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.
B. Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset.
C. Write a query that preprocesses the data by using BigQuery Export the query results as CSV files and use those files to create a Vertex Al managed dataset.
D. Write a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source.

正解：D

解説：
The simplest and most efficient approach for preparing the data for AutoML is to use BigQuery and Vertex AI. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery can preprocess the data by using SQL functions such as filtering, aggregating, joining, transforming, and creating new features. The preprocessed data can be stored in a new table in BigQuery, which can be used as the data source for Vertex AI. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a BigQuery table, which can be used to train an AutoML model. Vertex AI can also evaluate, deploy, and monitor the AutoML model, and provide online or batch predictions. By using BigQuery and Vertex AI, users can leverage the power and simplicity of Google Cloud to train an AutoML model to predict house prices.
The other options are not as simple or efficient as option A, for the following reasons:
* Option B: Using Dataflow to preprocess the data and write the output in TFRecord format to a Cloud Storage bucket would require more steps and resources than using BigQuery and Vertex AI. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. TFRecord is a binary file format that can store sequential data efficiently. However, using Dataflow and TFRecord would require writing code, setting up a pipeline, choosing a runner, and managing the output files. Moreover, TFRecord is not a supported format for Vertex AI managed datasets, so the data would need to be converted to CSV or JSONL files before creating a Vertex AI managed dataset.
* Option C: Writing a query that preprocesses the data by using BigQuery and exporting the query results as CSV files would require more steps and storage than using BigQuery and Vertex AI. CSV is a text file format that can store tabular data in a comma-separated format. Exporting the query results as CSV files would require choosing a destination Cloud Storage bucket, specifying a file name or a wildcard, and setting the export options. Moreover, CSV files can have limitations such as size, schema, and encoding, which can affect the quality and validity of the data. Exporting the data as CSV files would also incur additional storage costs and reduce the performance of the queries.
* Option D: Using a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library and exporting the data as CSV files would require more steps and skills than using BigQuery and Vertex AI. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. Pandas is a popular Python library that can manipulate and analyze data in a tabular format.
However, using Vertex AI Workbench and pandas would require creating a notebook instance, writing Python code, installing and importing pandas, connecting to BigQuery, loading and preprocessing the data, and exporting the data as CSV files. Moreover, pandas can have limitations such as memory usage, scalability, and compatibility, which can affect the efficiency and reliability of the data processing.
References:
Preparing for Google Cloud Certification: Machine Learning Engineer , Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML Google Cloud Professional Machine Learning Engineer Exam Guide , Section 1: Architecting low-code ML solutions, 1.3 Training models by using AutoML Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Low-code ML Solutions, Section 4.3: AutoML BigQuery Vertex AI Dataflow TFRecord CSV Vertex AI Workbench Pandas

質問 # 63
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Professional-Machine-Learning-Engineer試験に実際に参加して資料を選択する前に、このような証明書を保持することの重要性を思い出してください。このようなProfessional-Machine-Learning-Engineer証明書を取得することで、昇給、昇進の機会、上司や同僚からの信頼など、将来の多くの同意結果を習得できます。これらすべての快い結果は、もはやあなたにとって夢ではありません。そして、Professional-Machine-Learning-Engineer試験準備により、成績を改善し、生活の状態を変え、キャリアの驚くべき変化を得ることができ、すべてが可能になります。それはすべて、Professional-Machine-Learning-Engineer学習の質問から始まります。

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