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

Section	Objectives
Topic 1: Data preparation and processing	- Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services - Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques
Topic 2: Designing ML solutions	- ML architecture design 1. Design scalable ML systems on GCP 2. Select appropriate ML models and approaches - Framing ML problems 1. Define success metrics and evaluation criteria 2. Translate business problems into ML tasks
Topic 3: ML pipeline automation and orchestration	- Pipeline design 1. Use Vertex AI Pipelines 2. Build end-to-end ML pipelines
Topic 4: Deployment and operations	- Monitoring and maintenance 1. Monitor model drift and performance 2. Retraining and lifecycle management - Model deployment 1. Batch and online prediction systems 2. Deploy models using Vertex AI endpoints
Topic 5: ML model development	- Evaluation 1. Model validation strategies 2. Evaluate model performance metrics - Model training and tuning 1. Hyperparameter tuning and optimization 2. Train models using TensorFlow / Vertex AI

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Google Professional Machine Learning Engineer Sample Questions (Q82-Q87):

NEW QUESTION # 82
You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
B. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
C. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
D. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

Answer: D

Explanation:
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources.

NEW QUESTION # 83
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 CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
B. 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
C. 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.
D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

Answer: A

NEW QUESTION # 84
You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch.
After a review by an AI Ethics team, you are approved to start development. Each video asset in your company's catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event data. How should you build the recommendation system for the first version of the product?

A. Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.
B. Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.
C. Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.
D. Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.

Answer: B

Explanation:
https://developers.google.com/machine-learning/guides/rules-of-ml

NEW QUESTION # 85
You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

A. Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.
B. Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.
C. Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.
D. Use the func_to_container_op function to create custom components from the Python code.

Answer: D

Explanation:
The func_to_container_op function in the Kubeflow Pipelines SDK is specifically designed to convert Python functions into containerized components that can be executed in a Kubernetes cluster. By using this function, the Data Science team can easily integrate their custom Python code into the Kubeflow Pipelines SDK without having to learn the details of containerization or Kubernetes.

NEW QUESTION # 86
You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

A. Convert the categorical string data to one-hot hash buckets.
B. Convert each categorical value into an integer value.
C. Map the categorical variables into a vector of boolean values.
D. Convert each categorical value into a run-length encoded string.

Answer: C

NEW QUESTION # 87
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