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To become a certified Google Professional Machine Learning Engineer, candidates must demonstrate proficiency in several key areas, including data preparation and processing, machine learning model development and implementation, and business problem-solving. Professional-Machine-Learning-Engineer Exam covers a broad range of topics, including data modeling, distributed computing, and machine learning frameworks such as TensorFlow and Keras. Additionally, candidates must be familiar with the tools and technologies used in machine learning, including Google Cloud Platform services such as BigQuery, Dataflow, and Kubernetes.

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The Google Professional-Machine-Learning-Engineer Exam covers a wide range of topics, including data preparation and feature engineering, model selection and training, hyperparameter tuning, model evaluation, and deployment of machine learning models on Google Cloud. Candidates are expected to have a solid understanding of machine learning algorithms, statistical analysis, and data visualization.

Google Professional Machine Learning Engineer exam is an advanced-level certification and requires a deep understanding of machine learning concepts and practices. To be eligible for this certification, individuals must have experience with machine learning frameworks, such as TensorFlow and Scikit-learn, and have the ability to use these frameworks to create machine learning models. Additionally, individuals must have experience with data preprocessing and data analysis, as well as experience with cloud computing, specifically on the Google Cloud Platform.

Google Professional Machine Learning Engineer Sample Questions (Q198-Q203):

NEW QUESTION # 198
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

A. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 100.
B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100
C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 1.
D. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1

Answer: C

NEW QUESTION # 199
You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

A. Create a Vertex Al experiment, and enable autologging inside the custom job
B. Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.
C. Use the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.
D. Register each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

Answer: D

NEW QUESTION # 200
You work on a growing team of more than 50 data scientists who all use Al 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. 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
B. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
C. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.
D. 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.

Answer: A

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. For example, you can label jobs by team (such as engineering or research) and development phase (prod or test), then filter the jobs based on the team and phase. Labels are also available on operations, but these labels are derived from the resource to which the operation applies. You cannot add or update labels on an operation.
https://cloud.google.com/ai-platform/prediction/docs/sharing-models.

NEW QUESTION # 201
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?

A. Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.
B. Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri environment variable to read the images by using the tf. data. Dataset. Iist_flies function.
C. Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.
D. Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

Answer: A

NEW QUESTION # 202
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. 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. 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
D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.
E. 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.

Answer: A

NEW QUESTION # 203
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Latest Braindumps Professional-Machine-Learning-Engineer Book, Test Professional-Machine-Learning-Engineer Valid

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Google Professional Machine Learning Engineer Sample Questions (Q198-Q203):

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