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Google Associate Data Practitioner Sample Questions (Q19-Q24):
NEW QUESTION # 19
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?
- A. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
- B. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
- C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
- D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
Answer: C
NEW QUESTION # 20
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?
- A. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
- B. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
- C. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
- D. Use authorized views to share query results with the payroll specialist.
Answer: D
NEW QUESTION # 21
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?
- A. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- B. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
- C. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
- D. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
Answer: D
NEW QUESTION # 22
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?
- A. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
- B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
- C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
- D. Apply a filter to only show products with a positive profit margin.
Answer: B
NEW QUESTION # 23
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?
- A. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
- B. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
- C. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
- D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
Answer: B
NEW QUESTION # 24
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