RELIABLE FREE ASSOCIATE-DATA-PRACTITIONER EXAM DUMPS HELP YOU TO GET ACQUAINTED WITH REAL ASSOCIATE-DATA-PRACTITIONER EXAM SIMULATION

Reliable Free Associate-Data-Practitioner Exam Dumps Help You to Get Acquainted with Real Associate-Data-Practitioner Exam Simulation

Reliable Free Associate-Data-Practitioner Exam Dumps Help You to Get Acquainted with Real Associate-Data-Practitioner Exam Simulation

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Google Cloud Associate Data Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

  • A. Use Storage Transfer Service.
  • B. Use Cloud Storage FUSE.
  • C. Use a Transfer Appliance.
  • D. Use gcloud storage commands.

Answer: A

Explanation:
Using the Storage Transfer Service is the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.


NEW QUESTION # 39
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

  • A.
  • B.
  • C.
  • D.

Answer: B

Explanation:
In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should:
Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input.
Rename the target label column to label, as BigQuery ML requires the target column to be named label.
The chosen query satisfies these requirements:
SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label.
The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained.
This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.


NEW QUESTION # 40
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

  • A. Use Colab Enterprise to develop a custom model for purchase prediction.
  • B. Use Vertex AI Workbench to develop a custom model for purchase prediction.
  • C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
  • D. Use BigQuery ML to create a logistic regression model for purchase prediction.

Answer: D

Explanation:
Using BigQuery ML is the best solution in this case because:
Ease of use: BigQuery ML allows users to build machine learning models using SQL, which requires minimal ML expertise.
Integrated platform: Since the data already exists in BigQuery, there's no need to move it to another service, saving time and engineering resources.
Logistic regression: This is an appropriate model for binary classification tasks like predicting the likelihood of a customer making a purchase in the next month.


NEW QUESTION # 41
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?

  • A. Use Google-managed encryption keys (GMEK).
  • B. Use customer-supplied encryption keys (CSEK).
  • C. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
  • D. Use customer-managed encryption keys (CMEK).

Answer: A

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.


NEW QUESTION # 42
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

  • A. Store the data in Cloud Storage using Standard storage.
  • B. Store the data in Cloud Storage using Archive storage.
  • C. Store the data in Cloud Storaqe usinq Coldline storaqe.
  • D. Store the data in Cloud Storaqe usinq Nearline storaqe.

Answer: D

Explanation:
Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.


NEW QUESTION # 43
......

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