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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 3

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.

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Google Cloud Associate Data Practitioner Sample Questions (Q91-Q96):

NEW QUESTION # 91
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 data. 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. 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.

Answer: C

Explanation:
UsingTransfer Appliancesis the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.

NEW QUESTION # 92
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
C. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

Answer: A

Explanation:
Using aCloud Run functiontriggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.
The goal is to load small CSV files into BigQuery upon arrival (event-driven) with minimal latency, cost, and maintenance. Google Cloud provides serverless, event-driven options that align with this requirement. Let's evaluate each option in detail:
Option A: Cloud Composer (managed Apache Airflow) can schedule a pipeline to check Cloud Storage every
10 minutes, but this polling approach introduces latency (up to 10 minutes) and incurs costs for running Composer even when no files arrive. Maintenance includes managing DAGs and the Composer environment, which adds overhead. This is better suited for scheduled batch jobs, not event-driven ingestion.
Option B: A Cloud Run function triggered by a Cloud Storage event (via Eventarc or Pub/Sub) loads files into BigQuery as soon as they arrive, minimizing latency. Cloud Run is serverless, scales to zero when idle (low cost), and requires minimal maintenance (deploy and forget). Using the BigQuery API in the function (e.g., Python client library) handles small CSV loads efficiently. This aligns with Google's serverless, event-driven best practices.
Option C: Dataproc with Spark is designed for large-scale, distributed processing, not small CSV ingestion. It requires cluster management, incurs higher costs (even with ephemeral clusters), and adds unnecessary complexity for a simple load task.
Option D: The bq command-line tool in Cloud Shell is manual and not automated, failing the "upon arrival" requirement. It's a one-off tool, not a pipeline solution, and Cloud Shell isn't designed for persistent automation.
Why B is Best: Cloud Run leverages Cloud Storage's object creation events, ensuring near-zero latency between file arrival and BigQuery ingestion. It's serverless, meaning no infrastructure to manage, and costs scale with usage (free when idle). For small CSVs, the BigQuery load job is lightweight, avoiding processing overhead.
Extract from Google Documentation: From "Triggering Cloud Run with Cloud Storage Events" (https://cloud.
google.com/run/docs/triggering/using-events): "You can trigger Cloud Run services in response to Cloud Storage events, such as object creation, using Eventarc. This serverless approach minimizes latency and maintenance, making it ideal for real-time data pipelines." Additionally, from "Loading Data into BigQuery" (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-csv): "Programmatically load CSV files from Cloud Storage using the BigQuery API, enabling automated ingestion with minimal overhead."

NEW QUESTION # 93
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

Answer: D

Explanation:
In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should:
Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input.
Rename the target label column to label, as BigQuery ML requires the target column to be named label.
The chosen query satisfies these requirements:
SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label.
The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained.
This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.

NEW QUESTION # 94
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
D. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

Answer: D

Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.

NEW QUESTION # 95
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

A. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
B. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
C. Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.
D. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

Answer: D

Explanation:
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using Gemini to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the Gemini model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.

NEW QUESTION # 96
......

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Carl Gray Carl Gray

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