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Advanced examination information

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Google Associate Data Practitioner Sample Questions:

1. 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) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
C) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
D) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

2. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
B) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
C) Create a Cloud Run function that runs daily and deletes files older than seven days.
D) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

3. 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 the gcloud storage command to move the data to Cloud Storage.
B) Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
C) 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.
D) Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

4. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
B) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
C) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
D) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

5. You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A) Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B) Grant each partner read access to the BigQuery dataset by using 1AM roles.
C) Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D) Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.

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