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[General] Free Associate-Data-Practitioner Download Pdf, Associate-Data-Practitioner Test

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【General】 Free Associate-Data-Practitioner Download Pdf, Associate-Data-Practitioner Test

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Google Associate-Data-Practitioner Exam Syllabus Topics:
TopicDetails
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 (Q34-Q39):NEW QUESTION # 34
Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of dat a. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?
  • A. Create authorized views in the team's Google Cloud project that is only accessible by the team.
  • B. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.
  • C. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
  • D. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
Answer: D
Explanation:
Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance.

NEW QUESTION # 35
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?
  • A. 1. Cloud Composer
    2. Cloud SQL for MySQL
  • B. 1. BigQuery
    2. Analytics Hub
  • C. 1. Dataflow
    2. BigQuery
  • D. 1. Dataproc Serverless
    2. Bigtable
Answer: C
Explanation:
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to useDataflowandBigQuery.Dataflowis a fully managed service for real-time data processing and transformation, whileBigQueryis a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

NEW QUESTION # 36
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. Require users to specify a partition filter using the alter table statement in SQL.
  • B. Create a view that filters out rows that are older than one year.
  • C. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
  • D. 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.
Answer: C
Explanation:
Setting thetable partition expiration periodto one year using theALTER TABLEstatement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.
Extract from Google Documentation: From "Managing Partitioned Tables in BigQuery" (https://cloud.
google.com/bigquery/docs/partitioned-tables#expiration):"Set a partition expiration time using ALTER TABLE to automatically remove partitions older than a specified duration, reducing storage costs efficiently for ingestion-time partitioned tables."

NEW QUESTION # 37
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?
  • A. Create a temporary table.
  • B. Create an external table.
  • C. Create an object table.
  • D. Create a native table.
Answer: C
Explanation:
To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.

NEW QUESTION # 38
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?
  • A. Enable access control by using IAM roles.
  • B. Export the data to Cloud Storage, and use signed URLs to authorize access.
  • C. Encrypt the data by using customer-managed encryption keys (CMEK).
  • D. Update dataset privileges by using the SQL GRANT statement.
Answer: B
Explanation:
Using IAM roles to enable access control in BigQuery is the best approach to ensure that only authorized personnel can query the sensitive customer data. IAM allows you to define granular permissions at the project, dataset, or table level, ensuring that users have only the access they need in accordance with the principle of least privilege. For example, you can assign roles like roles/bigquery.dataViewer to allow read-only access or roles/bigquery.dataEditor for more advanced permissions. This approach provides centralized and manageable access control, which is critical for protecting sensitive data.

NEW QUESTION # 39
......
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