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NEW QUESTION 34
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can consume data from other Google Cloud servicesB. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sourcesC. Dataflow pipelines can be programmed in JavaD. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Answer: D

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs

 

NEW QUESTION 35
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

A. Use a higher-memory node so that the job runs fasterB. Use pre-emptible virtual machines (VMs) for the clusterC. Migrate the workload to Google Cloud DataflowD. Use SSDs on the worker nodes so that the job can run faster

Answer: C

 

NEW QUESTION 36
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A. You expect future mutations to have different features from the mutated samples in the database.B. You expect future mutations to have similar features to the mutated samples in the database.C. There are roughly equal occurrences of both normal and mutated samples in the database.D. You already have labels for which samples are mutated and which are normal in the database.E. There are very few occurrences of mutations relative to normal samples.

Answer: A,C

 

NEW QUESTION 37
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.B. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.C. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.D. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

Answer: B

 

NEW QUESTION 38
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

A. A good use for the wide and deep model is a small-scale linear regression problem.B. The wide model is used for memorization, while the deep model is used for generalization.C. The wide model is used for generalization, while the deep model is used for memorization.D. A good use for the wide and deep model is a recommender system.

Answer: B,D

Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

 

NEW QUESTION 39
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