71. A company stores customer data tables that include customer addresses in an AWS Lake Formation data lake. To comply with new regulations, the company must ensure that users cannot access data for customers who are in Canada.
The company needs a solution that will prevent user access to rows for customers who are in Canada.
Which solution will meet this requirement with the LEAST operational effort?
A. Set a row-level filter to prevent user access to a row where the country is Canada.
B. Create an IAM role that restricts user access to an address where the country is Canada.
C. Set a column-level filter to prevent user access to a row where the country is Canada.
D. Apply a tag to all rows where Canada is the country. Prevent user access where the tag is equal to “Canada”.
Answer
A
72. A company has implemented a lake house architecture in Amazon Redshift. The company needs to give users the ability to authenticate into Redshift query editor by using a third-party identity provider (IdP).
A data engineer must set up the authentication mechanism.
What is the first step the data engineer should take to meet this requirement?
A. Register the third-party IdP as an identity provider in the configuration settings of the Redshift cluster.
B. Register the third-party IdP as an identity provider from within Amazon Redshift.
C. Register the third-party IdP as an identity provider for AVS Secrets Manager. Configure Amazon Redshift to use Secrets Manager to manage user credentials.
D. Register the third-party IdP as an identity provider for AWS Certificate Manager (ACM). Configure Amazon Redshift to use ACM to manage user credentials.
Answer
B
73. A company uploads .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?
A. Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.
B. Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.
C. Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.
D. Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.
Answer
A
74. A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?
A. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.
B. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company’s data catalog as an external data catalog.
C. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company’s data catalog.
D. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company’s data catalog.
Answer
B
75. A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the fact table.
Which solution will meet these requirements?
A. Use multiple COPY commands to load the data into the Redshift cluster.
B. Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.
C. Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.
D. Use a single COPY command to load the data into the Redshift cluster.
Answer
D
76. A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
A. Use Amazon Macie pattern matching as part of the ETL job.
B. Train and use the AWS Glue PySpark Filter class in the ETL job.
C. Partition tables and use the ETL job to partition the data on a unique identifier.
D. Train and use the AWS Lake Formation FindMatches transform in the ETL job.
Answer
D
77. A data engineer is using an AWS Glue crawler to catalog data that is in an Amazon S3 bucket. The S3 bucket contains both .csv and json files. The data engineer configured the crawler to exclude the .json files from the catalog.
When the data engineer runs queries in Amazon Athena, the queries also process the excluded .json files. The data engineer wants to resolve this issue. The data engineer needs a solution that will not affect access requirements for the .csv files in the source S3 bucket.
Which solution will meet this requirement with the SHORTEST query times?
A. Adjust the AWS Glue crawler settings to ensure that the AWS Glue crawler also excludes .json files.
B. Use the Athena console to ensure the Athena queries also exclude the .json files.
C. Relocate the .json files to a different path within the S3 bucket.
D. Use S3 bucket policies to block access to the .json files.
Answer
C
78. A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function’s execution role to access the S3 bucket. However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?
A. The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.
B. The Lambda function is using an outdated SDK version, which caused the read failure.
C. The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.
D. The Lambda function’s execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.
Answer
D
79. Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository’s master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week’s scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?
A. git diff branchB master
git commit -m
B. git pull master
C. git rebase master
D. git fetch -b master
Answer
C
80. A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table? (Choose two.)
A. Select *from Employee where Region ID=’North America’;
B. Select *from Employee where Region ID=’North America’ and Department ID=20;
C. Select *from Employee where Department ID=20 and Region ID=’North America’;
D. Select *from Employee where Role ID=50;
E. Select *from Employee where Region ID=’North America’ and Role ID=50;
Answer
B, E