Question 1

You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

Options :

A : Write your data in TFRecords.

B : Z-normalize all the numeric features.

C : Oversample the fraudulent transaction 10 times.

D : Use one-hot encoding on all categorical features.

Answer: C

Question 2

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

Options :

A : Use the Natural Language API to classify support requests.

B : Use AutoML Natural Language to build the support requests classifier.

C : Use an established text classification model on AI Platform to perform transfer learning

D : Use an established text classification model on AI Platform as-is to classify support requests

Answer: C

Question 3

You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

Options :

A : Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc Save the preprocessed data as CSV files in a Cloud Storage bucket.

B : Load the data into a pandas DataFrame Implement the preprocessing steps using panda’s transformations. and train the model directly on the DataFrame.

C : Perform preprocessing in BigQuery by using SQL Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.

D : Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow Save the preprocessed data as CSV files in a Cloud Storage bucket.

Answer: C

Question 4

You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

Options :

A : Use the AI Platform Training built-in algorithms to create a custom model.

B : Use AutoMlL Natural Language to extract custom entities for classification.

C : Use the Cloud Natural Language API to extract custom entities for classification.

D : Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

Answer: B

Question 5

You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket. What should you do?

Options :

A : Use Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.

B : Use Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trains the model.

C : Import the labeled images as a managed dataset in Vertex AI and use AutoML to train the model.

D : Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and use BigQuery ML to train the model.

Answer: C

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