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NEW QUESTION 50
You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?
- A. The message body for the sensor event is too large.
- B. The Cloud Pub/Sub topic has too many messages published to it.
- C. Your custom endpoint is not acknowledging messages within the acknowledgement deadline.
- D. Your custom endpoint has an out-of-date SSL certificate.
Answer: C
Explanation:
Until or unless the message is not acknowledged within defined ack window period for every message, we will get duplicate (number of retries to send message can be defined).
https://cloud.google.com/pubsub/docs/troubleshooting#dupes
NEW QUESTION 51
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
- A. The wide model is used for generalization, while the deep model is used for memorization.
- B. The wide model is used for memorization, while the deep model is used for generalization.
- C. A good use for the wide and deep model is a small-scale linear regression problem.
- D. A good use for the wide and deep model is a recommender system.
Answer: B,D
Explanation:
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 52
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?
- A. Google Cloud Datastore
- B. Cloud Bigtable
- C. Google Cloud Storage
- D. Google BigQuery
Answer: B
Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp
NEW QUESTION 53
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems.
What should you do?
- A. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
- B. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review. - C. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
- D. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
Answer: B
NEW QUESTION 54
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