Google ADP

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Google ADP Exam Syllabus Topics:

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) 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.

2. You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A) Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
B) Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
C) Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
D) Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

3. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A) Use Cloud Tasks to schedule and run the jobs asynchronously.
B) Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C) Use Cloud Scheduler to schedule the jobs to run.
D) Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

4. Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A) Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
B) Create a view that filters out rows that are older than one year.
C) Require users to specify a partition filter using the alter table statement in SQL.
D) Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

5. You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A) Use the gcloud storage command to transfer the data over the internet.
B) Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D) Use Cloud Client Libraries to transfer the data over the internet.

Solutions: