ASSOCIATE-DATA-PRACTITIONER NEW DUMPS BOOK - HIGH PASS-RATE GOOGLE ASSOCIATE-DATA-PRACTITIONER VALID VCE: GOOGLE CLOUD ASSOCIATE DATA PRACTITIONER

Associate-Data-Practitioner New Dumps Book - High Pass-Rate Google Associate-Data-Practitioner Valid Vce: Google Cloud Associate Data Practitioner

Associate-Data-Practitioner New Dumps Book - High Pass-Rate Google Associate-Data-Practitioner Valid Vce: Google Cloud Associate Data Practitioner

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Google Cloud Associate Data Practitioner Sample Questions (Q33-Q38):

NEW QUESTION # 33
You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

  • A. Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.
  • B. Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.
  • C. Use Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.
  • D. Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

Answer: C

Explanation:
Using Dataflow to implement a streaming pipeline triggered by an OBJECT_FINALIZE notification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.


NEW QUESTION # 34
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (Pll) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?

  • A. 1. Filestore
    2. Bigtable
    3. BigQuery
  • B. 1. Cloud Storage
    2. CloudSQL for PostgreSQL
    3. Bigtable
  • C. 1. Filestore
    2. Cloud SQL for PostgreSQL
    3. Datastore
  • D. 1. Cloud Storage
    2. BigQuery
    3. Firestore

Answer: D

Explanation:
Audio files from phone interactions: Use Cloud Storage. Cloud Storage is ideal for storing large binary objects like audio files, offering scalability and easy accessibility for training purposes.
CSV files of users' personally identifiable information (PII): Use BigQuery. BigQuery is a serverless data warehouse optimized for analyzing structured data, such as CSV files, using SQL. It ensures compliance with PII handling through access controls and data encryption.
A large volume of small document files: Use Firestore. Firestore is a scalable NoSQL database designed for applications requiring fast, real-time interactions and structured document storage, making it suitable for powering other applications.


NEW QUESTION # 35
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

  • A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
  • B. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
  • C. Use Cloud Run functions to trigger the Spark job and email the report.
  • D. Use Cloud Composer to orchestrate the Spark job and email the report.

Answer: B

Explanation:
Using Dataproc workflow templates is a fully-managed and straightforward solution for defining and scheduling your Spark job on a Dataproc cluster. Workflow templates allow you to automate the execution of Spark jobs with predefined steps, including data processing and report generation. You can integrate email notifications by adding a step to the workflow that sends the report using tools like a Cloud Function or external email service. This approach minimizes complexity while leveraging Dataproc's managed capabilities for batch processing.


NEW QUESTION # 36
You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues. What should you do?

  • A. Load the CSV file as a table in BigQuery, and use scheduled queries to run SQL transformation scripts.
  • B. Load the CSV file as a table in BigQuery. Create a batch pipeline in Cloud Data Fusion by using a BigQuery source and sink.
  • C. Create a batch pipeline in Cloud Data Fusion by using a Cloud Storage source and a BigQuery sink.
  • D. Create a batch pipeline in Dataflow by using the Cloud Storage CSV file to BigQuery batch template.

Answer: C

Explanation:
Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:
Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.
Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.
Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.


NEW QUESTION # 37
You work for a global financial services company that trades stocks 24/7. You have a Cloud SGL for PostgreSQL user database. You need to identify a solution that ensures that the database is continuously operational, minimizes downtime, and will not lose any data in the event of a zonal outage. What should you do?

  • A. Continuously back up the Cloud SGL instance to Cloud Storage. Create a Compute Engine instance with PostgreSCL in a different region. Restore the backup in the Compute Engine instance if a failure occurs.
  • B. Configure and create a high-availability Cloud SQL instance with the primary instance in zone A and a secondary instance in any zone other than zone A.
  • C. Create a read replica in another region. Promote the replica to primary if a failure occurs.
  • D. Create a read replica in the same region but in a different zone.

Answer: B

Explanation:
Configuring a high-availability (HA) Cloud SQL instance ensures continuous operation, minimizes downtime, and prevents data loss in the event of a zonal outage. In this setup, the primary instance is located in one zone (e.g., zone A), and a synchronous secondary instance is located in a different zone within the same region. This configuration ensures that all data is replicated to the secondary instance in real-time. In the event of a failure in the primary zone, the system automatically promotes the secondary instance to primary, ensuring seamless failover with no data loss and minimal downtime. This is the recommended approach for mission-critical, highly available databases.


NEW QUESTION # 38
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