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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q226-Q231):
NEW QUESTION # 226
Select the Correct statements with regard to using Federated authentication/SSO?
- A. Snowflake supports using SSO with organizations, and you can use the corresponding URL in the SAML2 security integration.
- B. Snowflake supports multiple audience values (i.e. Audience or Audience Restriction Fields) in the SAML 2.0 assertion from the identity provider to Snowflake.
- C. Snowflake supports SSO with Private Connectivity to the Snowflake Service for Snow-flake accounts on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
- D. Snowflake supports using MFA in conjunction with SSO to provide additional levels of security.
Answer: A,B,C,D
NEW QUESTION # 227
Which of the following best describes the type of data found in traditional relational databases?
- A. Free-form data
- B. Unstructured data
- C. Structured data
- D. Semi-structured data
Answer: C
NEW QUESTION # 228
A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Which solution will meet this requirement MOST cost-effectively?
- A. Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.
- B. Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.
- C. Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.
- D. Use Amazon Athena Federated Query to join the data from all data sources.
Answer: D
Explanation:
You can query these sources by using Federated Queries, which is a native feature of Athena.
The other options may increase costs and operational overhead, as they use more than one service to achieve the same result.
https://docs.aws.amazon.com/athena/latest/ug/connectors-available.html
NEW QUESTION # 229
A company has as JSON file that contains personally identifiable information (PII) data and non- PII data. The company needs to make the data available for querying and analysis.
The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.
- B. Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.
- C. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation.
Use Lake Formation permissions to provide access to the required data based on the type of user. - D. Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.
Answer: C
Explanation:
AWS Lake Formation lets you register your JSON file as a table in the data catalog and apply fine-grained permissions (down to columns or rows) without moving or transforming the underlying data. You can grant all employees access to the non-PII columns while restricting the PII columns to a small group, achieving your security requirements with minimal operational overhead.
NEW QUESTION # 230
A company runs a multi-tenant Amazon EMR cluster on Amazon EC2 instances. Multiple teams perform interactive query analyses and data transformations on the data in the EMR cluster. The teams can access the cluster only through EMR Studio workspaces and EMR steps.
The teams need to use EMR steps to run Apache Spark jobs to fetch data from an Amazon DynamoDB table. The DynamoDB table contains confidential data that must be accessible to only one specific team. The company needs to ensure that only the appropriate team can access the confidential data in the EMR cluster. Which solution will meet these requirements?
- A. Set up runtime roles for EMR steps.
- B. Set up AWS Lake Formation permissions.
- C. Set up IAM roles for EMR File System (EMRFS) requests.
- D. Set up a DynamoDB resource-based policy.
Answer: A
Explanation:
Runtime roles for EMR steps allow each submitted step (such as a Spark job) to assume a distinct IAM role at execution time. By granting DynamoDB read permissions only to the role used by the authorized team's steps, the confidential table data becomes accessible only to that team's Spark jobs, while other teams' steps run with roles that lack access.
NEW QUESTION # 231
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