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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic	Details
Topic 1	Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services
Topic 2	Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.\| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 3	Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.

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Google Cloud Associate Data Practitioner Sample Questions (Q67-Q72):

NEW QUESTION # 67
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. Apply a filter to only show products with a positive profit margin.
B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
D. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Defining a new measure in LookML is the most efficient and direct way to calculate and visualize aggregated metrics like profit margin.
Measures are designed for calculations based on existing fields.
Why other options are incorrect:A: Filtering doesn't calculate or visualize the profit margin itself.
C: Dimensions are for categorizing data, not calculating aggregated metrics.
D: Derived tables are more complex and unnecessary for a simple calculation like profit margin, which can be done using a measure.

NEW QUESTION # 68
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. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D. Create a Cloud Run function that runs daily and deletes files older than seven days.

Answer: A

Explanation:
Configuring a Cloud Storage lifecycle rule to automatically delete objects older than seven days is the best solution because:
Built-in feature: Cloud Storage lifecycle rules are specifically designed to manage object lifecycles, such as automatically deleting or transitioning objects based on age.
No additional setup: It requires no external services or custom code, reducing complexity and maintenance.
Cost-effective: It directly achieves the goal of deleting files after seven days without incurring additional compute costs.

NEW QUESTION # 69
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Answer: A

Explanation:
Pushing event information to aPub/Sub topicand then creating aDataflow job using the Dataflow job builderis the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations isA. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here's why:
* Pub/Sub and Dataflow:
* Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
* Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
* The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
* Dataflow is built for real time streaming and applying transformations.
Let's break down why the other options are less suitable:
* B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations:
* This is a batch processing approach, not real-time.
* Cloud Storage and scheduled jobs are not designed for near real-time analysis.
* This does not meet the real time requirement of the question.
* C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery:
* While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
* Cloud run does not provide a visual interface.
* D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
* BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
* This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.

NEW QUESTION # 70
You are responsible for managing Cloud Storage buckets for a research company. Your company has well- defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?

A. Configure the buckets to use the Archive storage class.
B. Configure the buckets to use the Standard storage class and enable Object Versioning.
C. Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
D. Configure the buckets to use the Autoclass feature.

Answer: C

Explanation:
Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well- defined data tiering and retention needs, providing cost efficiency and automation.
Extract from Google Documentation: From "Object Lifecycle Management" (https://cloud.google.com
/storage/docs/lifecycle):"Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."

NEW QUESTION # 71
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 platformsYou 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?

A. Extract 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.
B. Copy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.
C. Extract 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.
D. Extract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
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.

NEW QUESTION # 72
......

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