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>> Top Associate-Data-Practitioner Questions <<
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NEW QUESTION # 63
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?
Answer: B
Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.
NEW QUESTION # 64
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?
Answer: B
Explanation:
Pushing event information to aPub/Sub topicand then creating aDataflow job using the Dataflow job builderis the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations isA. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here's why:
* Pub/Sub and Dataflow:
* Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
* Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
* The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
* Dataflow is built for real time streaming and applying transformations.
Let's break down why the other options are less suitable:
* B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations:
* This is a batch processing approach, not real-time.
* Cloud Storage and scheduled jobs are not designed for near real-time analysis.
* This does not meet the real time requirement of the question.
* C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery:
* While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
* Cloud run does not provide a visual interface.
* D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
* BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
* This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.
NEW QUESTION # 65
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?
Answer: C
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.
NEW QUESTION # 66
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Storage Transfer Service with agent-based transfer allows for online migrations and provides the ability to set bandwidth limits.
Agents are installed on-premises and can be configured to respect network constraints.
Why other options are incorrect:B: The --daisy-chain option is not related to bandwidth control.
C: Transfer Appliance is for offline migrations and is not suitable for online transfers with bandwidth constraints.
D: The --no-clobber option prevents overwriting existing files but does not control bandwidth.
NEW QUESTION # 67
You are building a batch data pipeline to process 100 GB of structured data from multiple sources for daily reporting. You need to transform and standardize the data prior to loading the data to ensure that it is stored in a single dataset. You want to use a low-code solution that can be easily built and managed. What should you do?
Answer: A
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Cloud Data Fusion is a fully managed, cloud-native data integration service for building and managing ETL/ELT data pipelines.
It provides a graphical interface for building pipelines without coding, making it a low-code solution.
Cloud data fusion is perfect for the ingestion, transformation and loading of data into BigQuery.
Why other options are incorrect:A: Looker studio is for visualization, not data transformation.
C: Cloud SQL is a relational database, not ideal for large-scale analytical data.
D: Cloud run is for stateless applications, not batch data processing.
NEW QUESTION # 68
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