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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 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 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 (Q107-Q112):

NEW QUESTION # 107
You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

A. Migrate the database to a larger Cloud SQL instance.
B. Store frequently accessed data in a Memorystore instance.
C. Use Cloud CDN to cache frequently accessed data.
D. Enable automatic backups, and create a read replica of the Cloud SQL instance.

Answer: D

Explanation:
Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

NEW QUESTION # 108
You work for a gaming company that collects real-time player activity data. This data is streamed into Pub
/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. Youneed to design a pipeline that ensures low latency and high throughput while following a Google-recommended approach.
What should you do?

A. Use Cloud Composer to orchestrate a workflow that reads the data from Pub/Sub, processes the data using a Python script, and writes it to BigQuery.
B. Use Dataproc to create an Apache Spark streaming job that reads the data from Pub/Sub, processes the data, and writes it to BigQuery.
C. Use Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming API.
D. Use Dataflow to create a streaming pipeline that reads the data from Pub/Sub, processes the data, and writes it to BigQuery using the streaming API.

Answer: D

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is the recommended service for real-time stream processing on Google Cloud.
It provides scalable and reliable processing with low latency and high throughput.
Dataflow's streaming API is optimized for Pub/Sub integration and BigQuery streaming inserts.
Why other options are incorrect:A: Cloud Composer is for batch orchestration, not real-time streaming.
B: Dataproc and Spark streaming are more complex and not as efficient as Dataflow for this task.
D: Cloud Run functions are for stateless, event-driven applications, not continuous stream processing.

NEW QUESTION # 109
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B. Migrate the Spark jobs to Dataproc on Compute Engine.
C. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

Explanation:
Migrating the Spark jobs toDataproc Serverlessis the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.

NEW QUESTION # 110
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. Store all data in a single BigQuery table without partitioning or lifecycle policies.
B. 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.
C. 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.
D. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Answer: C

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 # 111
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

A. Use Google-managed encryption keys (GMEK).
B. Use customer-supplied encryption keys (CSEK).
C. Use customer-managed encryption keys (CMEK).
D. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

Answer: A

Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.

NEW QUESTION # 112
......

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Associate-Data-Practitioner study materials - Google Associate-Data-Practitioner dumps VCE

Google Associate-Data-Practitioner Exam Syllabus Topics:

Reliable Associate-Data-Practitioner Test Syllabus - Training Associate-Data-Practitioner Tools

Google Cloud Associate Data Practitioner Sample Questions (Q107-Q112):

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