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NEW QUESTION 40
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
- A. Option D
- B. Option C
- C. Option B.
- D. Option A
Answer: D
NEW QUESTION 41
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?
- A. Use the INSERT statement to insert a batch of data every 60 seconds.
- B. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
- C. Use the MERGE statement to apply updates in batch every 60 seconds.
- D. Use bq load to load a batch of sensor data every 60 seconds.
Answer: B
NEW QUESTION 42
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
- A. Dropout Methods
- B. Serialization
- C. Dimensionality Reduction
- D. Threading
Answer: A
Explanation:
Explanation/Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877
NEW QUESTION 43
You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into BigQuery. For security reasons, you decide to redact your customers’ Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?
- A. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.
- B. Use BigQuery’s built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.
- C. Use BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.
- D. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.
Answer: A
NEW QUESTION 44
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