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Google Associate-Data-Practitioner Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Certified - Associate Data Practitioner
Exam Number:	Associate Data Practitioner
Certificate Validity Period:	2 years
Available Languages:	Japanese, English
Real Exam Qty:	50-60
Related Certifications:	Google Cloud Certified - Associate Data Practitioner
Passing Score:	Not publicly disclosed by Google Cloud
Exam Duration:	120 minutes
Exam Format:	Multiple select, Multiple choice
Exam Price:	$120 USD
Sample Questions:	Google Associate-Data-Practitioner Sample Questions
Exam Way:	Online proctored exam or Onsite at a testing center
Pre Condition:	No formal prerequisites, but Google recommends hands-on experience with Google Cloud data services and foundational cloud knowledge.
Official Syllabus URL:	https://cloud.google.com/learn/certification/data-practitioner

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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.

Google Cloud Associate Data Practitioner Sample Questions (Q52-Q57):

NEW QUESTION # 52
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 data. You also want tocreate a reusable framework in case you need to share this data with other teams in the future. What should you do?

A. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
B. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.
C. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
D. Create authorized views in the team's Google Cloud project that is only accessible by the team.

Answer: C

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.
Extract from Google Documentation: From "Analytics Hub Overview" (https://cloud.google.com/analytics- hub/docs):"Analytics Hub enables secure, controlled data sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent data copying, providing a reusable framework for sharing BigQuery datasets across teams."

NEW QUESTION # 53
You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales dat a. You want to prevent data loss and reporting issues if the table is accidentally deleted. What should you do?

A. Create a clone of the table. On deletion, re-create the deleted table by copying the content of the clone.
B. Create a view of the table. On deletion, re-create the deleted table from the view and time travel data.
C. Configure the time travel duration on the table to be exactly seven days. On deletion, re-create the deleted table solely from the time travel data.
D. Schedule the creation of a new snapshot of the table once a week. On deletion, re-create the deleted table using the snapshot and time travel data.

Answer: D

Explanation:
Scheduling the creation of a snapshot of the table weekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery's time travel feature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.

NEW QUESTION # 54
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

A. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
B. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
C. Create a multi-region bucket, and upload the files to this bucket.
D. Create a dual-region bucket, and upload the files to this bucket.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
* Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It's fully automated and meets the requirement.
* Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
* Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don't let you specify exactly two regions, potentially violating the restriction.

NEW QUESTION # 55
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 customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
B. Use customer-supplied encryption keys (CSEK).
C. Use customer-managed encryption keys (CMEK).
D. Use Google-managed encryption keys (GMEK).

Answer: D

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.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.

NEW QUESTION # 56
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D. Grant each partner read access to the BigQuery dataset by using 1AM roles.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Analytics Hub allows you to share datasets with external partners while maintaining control over access.
Subscriptions allow granular control.
Why other options are incorrect:B: Cloud storage is for files, not bigquery datasets.
C: IAM roles do not allow for granular row and column level control.
D: Creating a separate project for each partner is complex and not scalable.

NEW QUESTION # 57
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