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NEW QUESTION 22
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?
- A. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
- B. The learning rate should be increased because the optimization process was trapped at a local minimum.
- C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
- D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: C
Explanation:
Explanation/Reference: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit
NEW QUESTION 23
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error. What should you do?
- A. Use batch prediction mode instead of online mode.
- B. Apply for a quota increase for the number of prediction requests.
- C. Use base64 to encode your data before using it for prediction.
- D. Send the request again with a smaller batch of instances.
Answer: C
NEW QUESTION 24
Your organization’s call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?
- A. 1 = Cloud Function, 2 = Cloud SQL
- B. 1 = Pub/Sub, 2 = Datastore
- C. 1 = Dataflow, 2 = Cloud SQL
- D. 1 = Dataflow, 2 = BigQuery
Answer: A
NEW QUESTION 25
You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?
- A. Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.
- B. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.
- C. Use AutoML to optimize the model’s recall in order to minimize false negatives.
- D. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
Answer: D
NEW QUESTION 26
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
- A. 501*256+257*128+2 = 161154
- B. 500*256+256*128+128*2 = 161024
- C. 500*256*0 25+256*128*0 25+128*2 = 40448
- D. 501*256+257*128+128*2=161408
Answer: C
NEW QUESTION 27
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