Google Associate Data Practitioner Exam Questions Instant Access

Question 1

You are designing an application that will interact with several BigQuery datasets. You need to grant the application's service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre-defined IAM role(s) should you apply to the service account?

Aroles/bigquery.jobUser and roles/bigquery.dataOwner

Broles/bigquery.connectionUser and roles/bigquery.dataViewer

Croles/bigquery.admin

Droles/bigquery.user and roles/bigquery.filteredDataViewer

Answer : A

roles/bigquery.jobUser:

This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.

From Google Cloud documentation: 'BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs.'

roles/bigquery.dataOwner:

This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.

From Google Cloud documentation: 'BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries.'

Why other options are incorrect:

B . roles/bigquery.connectionUser and roles/bigquery.dataViewer:

roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.

C . roles/bigquery.admin:

roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.

D . roles/bigquery.user and roles/bigquery.filteredDataViewer:

roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.

Principle of Least Privilege:

The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.

By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.

Google Cloud Documentation Reference:

BigQuery IAM roles: https://cloud.google.com/bigquery/docs/access-control-basic-roles

IAM best practices: https://cloud.google.com/iam/docs/best-practices-for-using-iam

Question 2

You work for a retail company that collects customer data from various sources:

Online transactions: Stored in a MySQL database

Customer feedback: Stored as text files on a company server

Social media activity: Streamed in real-time from social media platforms

You need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?

ACopy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.

BExtract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.

CExtract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.

DExtract and load the online transactions data into Bigtable. Import the customer feedback data into Cloud Storage. Store the social media activity in Cloud SQL for MySQL.

Answer : B

Comprehensive and Detailed In-Depth

The pipeline must extract diverse data types and load them into systems optimized for analysis and ML. Let's assess:

Option A: Cloud SQL for transactions keeps data relational but isn't ideal for analysis/ML (less scalable than BigQuery). BigQuery for feedback is fine but skips staging. Cloud Storage for streaming social media loses real-time context and requires extra steps for analysis.

Option B: BigQuery for transactions (via export from MySQL) supports analysis/ML with SQL. Cloud Storage stages feedback text files for preprocessing, then BigQuery ingestion. Pub/Sub and Dataflow stream social media into BigQuery, enabling real-time analysis---optimal for all sources.

Option C: Cloud Storage for all data is a staging step, not a final solution for analysis/ML, requiring additional pipelines.

Option D: Bigtable for transactions is for NoSQL workloads, not analytics. Cloud Storage for feedback is staging-only. Cloud SQL for streaming social media is impractical (not real-time optimized). Why B is Best: BigQuery is Google's analytics/ML hub (e.g., BigQuery ML). Staging feedback in Cloud Storage allows preprocessing, and streaming via Pub/Sub/Dataflow ensures real-time data in BigQuery. For example, MySQL data exports to BigQuery via bq load, feedback uploads to GCS, and Dataflow processes social media into BigQuery tables. Extract from Google Documentation: From 'Data Analytics on Google Cloud' (https://cloud.google.com/architecture/data-analytics): 'Load structured data (e.g., MySQL) and unstructured data (e.g., text) into BigQuery for analysis and ML, using Cloud Storage for staging and Pub/Sub with Dataflow for streaming real-time data like social media activity.' Reference: Google Cloud Documentation - 'BigQuery Data Ingestion' (https://cloud.google.com/bigquery/docs/loading-data).

Why B is Best: BigQuery is Google's analytics/ML hub (e.g., BigQuery ML). Staging feedback in Cloud Storage allows preprocessing, and streaming via Pub/Sub/Dataflow ensures real-time data in BigQuery. For example, MySQL data exports to BigQuery via bq load, feedback uploads to GCS, and Dataflow processes social media into BigQuery tables.

Extract from Google Documentation: From 'Data Analytics on Google Cloud' (https://cloud.google.com/architecture/data-analytics): 'Load structured data (e.g., MySQL) and unstructured data (e.g., text) into BigQuery for analysis and ML, using Cloud Storage for staging and Pub/Sub with Dataflow for streaming real-time data like social media activity.'

Question 3

Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

AEnable Object Versioning.

BChange the bucket storage class to Archive.

CSet a bucket retention policy.

DSet temporary object holds.

Answer : C

Comprehensive and Detailed in Depth

Why C is correct:Bucket retention policies are specifically designed to enforce a minimum retention period for objects within a Cloud Storage bucket. This ensures that data cannot be deleted or overwritten before the specified period.

Why other options are incorrect:A: Object versioning allows you to keep multiple versions of an object, but it doesn't guarantee a minimum retention period.

B: Changing the storage class to Archive is for cost optimization, not data retention enforcement.

D: Object holds are for legal holds, not general retention.

Cloud Storage Retention Policies: https://cloud.google.com/storage/docs/bucket-lock

Cloud Storage Object Versioning: https://cloud.google.com/storage/docs/object-versioning

Cloud Storage Storage Classes: https://cloud.google.com/storage/docs/storage-classes

Cloud Storage Object Holds: https://cloud.google.com/storage/docs/legal-holds

Question 4

You are working on a project that requires analyzing daily social media dat

a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.

You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

AManually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

BUse Cloud Run functions to transform and load the data into BigQuery.

CUse Dataflow to transform the data and write the transformed data to BigQuery.

DUse Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

Answer : C

Comprehensive and Detailed in Depth

Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.

Dataflow is googles recomended way to transform large datasets.

It is designed for parallel processing, making it suitable for large datasets.

Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.

B: Cloud Run functions are for stateless applications, not large data transformations.

D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.

Dataflow: https://cloud.google.com/dataflow/docs

Query successful

Question 5

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

ACreate a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

BLaunch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

CCreate external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

DUse the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Answer : C

Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.

Question 6

Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse. You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

ACreate a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

BCreate BigQuery scheduled queries to define the transformations in SQL.

CUse Dataform to define the transformations in SQLX.

DUse Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

Answer : C

Comprehensive and Detailed in Depth

Why C is correct:Dataform is a managed data transformation service that allows you to define data pipelines using SQL and JavaScript.

It provides version control, modular code development, and data quality checks.

Why other options are incorrect:A: Cloud Composer is an orchestration tool, not a data transformation tool.

B: Scheduled queries are not suitable for complex ETL pipelines.

D: Dataproc requires setting up a Spark cluster and writing code, which is more complex than using Dataform.

Dataform: https://cloud.google.com/dataform/docs

Question 7

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?

AExport the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

BUse BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

CCreate a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

DCopy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

Answer : B

Comprehensive and Detailed In-Depth

For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery's scalability within Colab Enterprise.

Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.

Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.

Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.

Option D: Copying 25 TB to local storage is infeasible due to size and cost. Extract from Google Documentation: From 'Using BigQuery with Colab Enterprise' (https://cloud.google.com/colab/docs/bigquery): 'You can use BigQuery magic commands (%%bigquery) in Colab Enterprise to execute SQL queries directly against BigQuery datasets, providing efficient access to large-scale data without moving it.' Reference: Google Cloud Documentation - 'Colab Enterprise with BigQuery' (https://cloud.google.com/colab/docs).

Extract from Google Documentation: From 'Using BigQuery with Colab Enterprise' (https://cloud.google.com/colab/docs/bigquery): 'You can use BigQuery magic commands (%%bigquery) in Colab Enterprise to execute SQL queries directly against BigQuery datasets, providing efficient access to large-scale data without moving it.'

Google Cloud Associate Data Practitioner Exam Questions