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NEW QUESTION 49
You trained a text classification model. You have the following SignatureDefs:
What is the correct way to write the predict request?
- A. data = json dumps({“signature_name”: f,serving_default”, “instances”: [[‘a’, ‘b’], [c ‘d’], [‘e T]]})
- B. data = json.dumps({“signature_name”: “serving_default’ “instances”: [fab’, ‘be1, ‘cd’]]})
- C. data = json dumps({“signature_name”: “serving_default”! “instances”: [[‘a’, ‘b’, “c”, ‘d’, ‘e’, ‘f’]]})
- D. data = json.dumps({“signature_name”: “serving_default, “instances”: [[‘a’, ‘b ‘c’1, [d ‘e T]]})
Answer: D
NEW QUESTION 50
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)
- A. Include a copy of the samples in the test dataset in the training dataset.
- B. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
- C. Add more deep trees to the random forest to enable the model to learn more features.
- D. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
- E. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
Answer: B,D
NEW QUESTION 51
You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
- A. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
- B. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
- C. Create a new view with BigQuery that does not include a column with city information
- D. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
Answer: A
NEW QUESTION 52
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. 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.
- C. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
- D. 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.
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 53
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- B. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
- C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- D. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
Answer: C
NEW QUESTION 54
……
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